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diff --git a/_sources/python_scripts/linear_models_feature_engineering_classification.py b/_sources/python_scripts/linear_models_feature_engineering_classification.py
index 9fd203f34..12a2997da 100644
--- a/_sources/python_scripts/linear_models_feature_engineering_classification.py
+++ b/_sources/python_scripts/linear_models_feature_engineering_classification.py
@@ -235,7 +235,10 @@ def plot_decision_boundary(model, title=None):
 # %%
 from sklearn.preprocessing import KBinsDiscretizer
 
-classifier = make_pipeline(KBinsDiscretizer(n_bins=5), LogisticRegression())
+classifier = make_pipeline(
+    KBinsDiscretizer(n_bins=5, encode="onehot"),  # already the default params
+    LogisticRegression(),
+)
 classifier
 
 # %%
@@ -279,15 +282,20 @@ def plot_decision_boundary(model, title=None):
 # We can see that the decision boundary is now smooth, and while it favors
 # axis-aligned decision rules when extrapolating in low density regions, it can
 # adopt a more curvy decision boundary in the high density regions.
-#
-# Note however, that the number of knots is a hyperparameter that needs to be
-# tuned. If we use too few knots, the model would underfit the data, as shown on
-# the moons dataset. If we use too many knots, the model would overfit the data.
-#
 # However, as for the binning transformation, the model still fails to separate
 # the data for the XOR dataset, irrespective of the number of knots, for the
 # same reasons: **the spline transformation is a feature-wise transformation**
 # and thus **cannot capture interactions** between features.
+#
+# Take into account that the number of knots is a hyperparameter that needs to be
+# tuned. If we use too few knots, the model would underfit the data, as shown on
+# the moons dataset. If we use too many knots, the model would overfit the data.
+#
+# ```{note}
+# Notice that `KBinsDiscretizer(encode="onehot")` and `SplineTransformer` do not
+# require additional scaling. Indeed, they can replace the scaling step for
+# numerical features: they both create features with values in the [0, 1] range.
+# ```
 
 # %% [markdown]
 #
diff --git a/appendix/notebook_timings.html b/appendix/notebook_timings.html
index a28d4c9be..f3547b35d 100644
--- a/appendix/notebook_timings.html
+++ b/appendix/notebook_timings.html
@@ -1004,9 +1004,9 @@ <h1>Notebook timings<a class="headerlink" href="#notebook-timings" title="Permal
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_models_feature_engineering_classification.html"><span class="doc">python_scripts/linear_models_feature_engineering_classification</span></a></p></td>
-<td><p>2023-10-20 14:15</p></td>
+<td><p>2023-10-26 11:59</p></td>
 <td><p>cache</p></td>
-<td><p>10.8</p></td>
+<td><p>10.37</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_models_regularization.html"><span class="doc">python_scripts/linear_models_regularization</span></a></p></td>
diff --git a/python_scripts/linear_models_feature_engineering_classification.html b/python_scripts/linear_models_feature_engineering_classification.html
index 1c78863b1..ea075427c 100644
--- a/python_scripts/linear_models_feature_engineering_classification.html
+++ b/python_scripts/linear_models_feature_engineering_classification.html
@@ -935,7 +935,10 @@ <h2>Engineering non-linear features<a class="headerlink" href="#engineering-non-
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">KBinsDiscretizer</span>
 
-<span class="n">classifier</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">KBinsDiscretizer</span><span class="p">(</span><span class="n">n_bins</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span> <span class="n">LogisticRegression</span><span class="p">())</span>
+<span class="n">classifier</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
+    <span class="n">KBinsDiscretizer</span><span class="p">(</span><span class="n">n_bins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">encode</span><span class="o">=</span><span class="s2">&quot;onehot&quot;</span><span class="p">),</span>  <span class="c1"># already the default params</span>
+    <span class="n">LogisticRegression</span><span class="p">(),</span>
+<span class="p">)</span>
 <span class="n">classifier</span>
 </pre></div>
 </div>
@@ -999,14 +1002,20 @@ <h2>Engineering non-linear features<a class="headerlink" href="#engineering-non-
 </div>
 <p>We can see that the decision boundary is now smooth, and while it favors
 axis-aligned decision rules when extrapolating in low density regions, it can
-adopt a more curvy decision boundary in the high density regions.</p>
-<p>Note however, that the number of knots is a hyperparameter that needs to be
-tuned. If we use too few knots, the model would underfit the data, as shown on
-the moons dataset. If we use too many knots, the model would overfit the data.</p>
-<p>However, as for the binning transformation, the model still fails to separate
+adopt a more curvy decision boundary in the high density regions.
+However, as for the binning transformation, the model still fails to separate
 the data for the XOR dataset, irrespective of the number of knots, for the
 same reasons: <strong>the spline transformation is a feature-wise transformation</strong>
 and thus <strong>cannot capture interactions</strong> between features.</p>
+<p>Take into account that the number of knots is a hyperparameter that needs to be
+tuned. If we use too few knots, the model would underfit the data, as shown on
+the moons dataset. If we use too many knots, the model would overfit the data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Notice that <code class="docutils literal notranslate"><span class="pre">KBinsDiscretizer(encode=&quot;onehot&quot;)</span></code> and <code class="docutils literal notranslate"><span class="pre">SplineTransformer</span></code> do not
+require additional scaling. Indeed, they can replace the scaling step for
+numerical features: they both create features with values in the [0, 1] range.</p>
+</div>
 </section>
 <section id="modeling-non-additive-feature-interactions">
 <h2>Modeling non-additive feature interactions<a class="headerlink" href="#modeling-non-additive-feature-interactions" title="Permalink to this heading">#</a></h2>
@@ -1084,7 +1093,7 @@ <h2>Modeling non-additive feature interactions<a class="headerlink" href="#model
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/d98cf10afde42d00cba794b3555d7e9ba000cebdef829967a70a33ccba1b60db.png" src="../_images/d98cf10afde42d00cba794b3555d7e9ba000cebdef829967a70a33ccba1b60db.png" />
+<img alt="../_images/96da411b5c4ebfaa3ebeb9c05c1fa91e8164f132b58558585e11ad4b7d55a671.png" src="../_images/96da411b5c4ebfaa3ebeb9c05c1fa91e8164f132b58558585e11ad4b7d55a671.png" />
 </div>
 </div>
 <p>The polynomial kernel approach would be interesting in cases were the
@@ -1120,7 +1129,7 @@ <h2>Modeling non-additive feature interactions<a class="headerlink" href="#model
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/a830e52976bbc92558a4865dcfe3ea4ee88afda4db3236c609b4f65dca9f6558.png" src="../_images/a830e52976bbc92558a4865dcfe3ea4ee88afda4db3236c609b4f65dca9f6558.png" />
+<img alt="../_images/fb409cbf68b13df4149fba8f25d820751b1e5ad85c2612c3fe73045a40c0c004.png" src="../_images/fb409cbf68b13df4149fba8f25d820751b1e5ad85c2612c3fe73045a40c0c004.png" />
 </div>
 </div>
 <p>The resulting decision boundary is <strong>smooth</strong> and can successfully separate
@@ -1197,7 +1206,7 @@ <h2>Multi-step feature engineering<a class="headerlink" href="#multi-step-featur
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/65904496ae44a4a185e7c69818a8751bd821541b100b26091cf76db157f5a3f6.png" src="../_images/65904496ae44a4a185e7c69818a8751bd821541b100b26091cf76db157f5a3f6.png" />
+<img alt="../_images/aa30de15e213b5786f4300f81791f9ae43dbe0b3edb40fcea1c7d5ec46154031.png" src="../_images/aa30de15e213b5786f4300f81791f9ae43dbe0b3edb40fcea1c7d5ec46154031.png" />
 </div>
 </div>
 <p>The decision boundary of this pipeline is smooth, but with axis-aligned
diff --git a/searchindex.js b/searchindex.js
index 7e9182e05..cb504cba3 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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"python_scripts/parameter_tuning_nested.py", "python_scripts/parameter_tuning_parallel_plot.py", "python_scripts/parameter_tuning_randomized_search.py", "python_scripts/parameter_tuning_sol_02.py", "python_scripts/parameter_tuning_sol_03.py", "python_scripts/trees_classification.py", "python_scripts/trees_dataset.py", "python_scripts/trees_ex_01.py", "python_scripts/trees_ex_02.py", "python_scripts/trees_hyperparameters.py", "python_scripts/trees_regression.py", "python_scripts/trees_sol_01.py", "python_scripts/trees_sol_02.py", "toc.md", "trees/slides.md", "trees/trees_classification_index.md", "trees/trees_hyperparameters_index.md", "trees/trees_intuitions_index.md", "trees/trees_module_intro.md", "trees/trees_module_take_away.md", "trees/trees_quiz_m5_01.md", "trees/trees_quiz_m5_02.md", "trees/trees_quiz_m5_03.md", "trees/trees_quiz_m5_04.md", "trees/trees_regression_index.md", "trees/trees_wrap_up_quiz.md", "tuning/parameter_tuning_automated_index.md", 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take-away", "\u2705 Quiz M7.01", "\u2705 Quiz M7.02", "\u2705 Quiz M7.03", "\u2705 Quiz M7.04", "\u2705 Quiz M7.05", "\ud83c\udfc1 Wrap-up quiz 7", "Classification metrics", "Regression metrics", "Caveats of feature selection", "Module overview", "Main take-away", "\u2705 Quiz", "Introduction", "\u2705 Quiz", "Intuitions on linear models", "Module overview", "Main take-away", "Non-linear feature engineering for linear models", "\u2705 Quiz M4.01", "\u2705 Quiz M4.02", "\u2705 Quiz M4.03", "Regularization in linear model", "\ud83c\udfa5 Intuitions on linear models", "\ud83c\udfc1 Wrap-up quiz 4", "\ud83c\udfa5 Intuitions on regularized linear models", "\u2705 Quiz Intro.01", "\ud83c\udfa5 Introducing machine-learning concepts", "\u2705 Quiz M2.03", "\ud83c\udfa5 Bias versus Variance", "\u2705 Quiz M2.02", "\ud83c\udfa5 Comparing train and test errors", "Bias versus variance trade-off", "Module overview", "Overfitting and underfitting", "Main take-away", "Validation and learning curves", "\ud83c\udfc1 Wrap-up quiz 2", "\u2705 Quiz M2.01", "\ud83c\udfa5 Overfitting and Underfitting", "Tabular data exploration", "\u2705 Quiz M1.01", "Fitting a scikit-learn model on numerical data", "\u2705 Quiz M1.02", "\ud83c\udfa5 Validation of a model", "Handling categorical data", "\u2705 Quiz M1.03", "\ud83c\udfa5 Visualizing scikit-learn pipelines in Jupyter", "Module overview", "Main take-away", "\ud83c\udfc1 Wrap-up quiz 1", "First look at our dataset", "\ud83d\udcdd Exercise M1.01", "\ud83d\udcc3 Solution for Exercise M1.01", "Model evaluation using cross-validation", "\ud83d\udcdd Exercise M1.02", "\ud83d\udcdd Exercise M1.03", "Working with numerical data", "First model with scikit-learn", "Preprocessing for numerical features", "\ud83d\udcc3 Solution for Exercise M1.02", "\ud83d\udcc3 Solution for Exercise M1.03", "Encoding of categorical variables", "Using numerical and categorical variables together", "\ud83d\udcdd Exercise M1.04", "\ud83d\udcdd Exercise M1.05", "\ud83d\udcc3 Solution for Exercise M1.04", "\ud83d\udcc3 Solution for Exercise M1.05", "Visualizing scikit-learn pipelines in Jupyter", "Comparing model performance with a simple baseline", "\ud83d\udcdd Exercise M2.01", "\ud83d\udcdd Exercise M7.01", "Sample grouping", "Effect of the sample size in cross-validation", "Nested cross-validation", "\ud83d\udcc3 Solution for Exercise M2.01", "\ud83d\udcc3 Solution for Exercise M7.01", "Stratification", "Non i.i.d. data", "Cross-validation framework", "Overfit-generalization-underfit", "The Ames housing dataset", "The bike rides dataset", "The blood transfusion dataset", "The California housing dataset", "Feature importance", "Adaptive Boosting (AdaBoost)", "Bagging", "\ud83d\udcdd Exercise M6.01", "\ud83d\udcdd Exercise M6.02", "\ud83d\udcdd Exercise M6.03", "\ud83d\udcdd Exercise M6.04", "Gradient-boosting decision tree (GBDT)", "Speeding-up gradient-boosting", "Hyperparameter tuning", "Introductory example to ensemble models", "Random forests", "\ud83d\udcc3 Solution for Exercise M6.01", "\ud83d\udcc3 Solution for Exercise M6.02", "\ud83d\udcc3 Solution for Exercise M6.03", "\ud83d\udcc3 Solution for Exercise M6.04", "\ud83d\udcdd Exercise 01", "Benefits of using feature selection", "Limitation of selecting feature using a model", "\ud83d\udcc3 Solution for Exercise 01", "\ud83d\udcdd Exercise M4.01", "\ud83d\udcdd Exercise M4.02", "\ud83d\udcdd Exercise M4.03", "Non-linear feature engineering for Logistic Regression", "Regularization of linear regression model", "\ud83d\udcc3 Solution for Exercise M4.01", "\ud83d\udcc3 Solution for Exercise M4.02", "\ud83d\udcc3 Solution for Exercise M4.03", "Linear regression using scikit-learn", "Non-linear feature engineering for Linear Regression", "Linear regression without scikit-learn", "Linear models for classification", "Beyond linear separation in classification", "Classification", "\ud83d\udcdd Exercise M7.02", "\ud83d\udcdd Exercise M7.03", "Regression", "\ud83d\udcc3 Solution for Exercise M7.02", "\ud83d\udcc3 Solution for Exercise M7.03", "\ud83d\udcdd Exercise M3.01", "\ud83d\udcdd Exercise M3.02", "Hyperparameter tuning by grid-search", "Set and get hyperparameters in scikit-learn", "Evaluation and hyperparameter tuning", "Analysis of hyperparameter search results", "Hyperparameter tuning by randomized-search", "\ud83d\udcc3 Solution for Exercise M3.01", "\ud83d\udcc3 Solution for Exercise M3.02", "Build a classification decision tree", "The penguins datasets", "\ud83d\udcdd Exercise M5.01", "\ud83d\udcdd Exercise M5.02", "Importance of decision tree hyperparameters on generalization", "Decision tree for regression", "\ud83d\udcc3 Solution for Exercise M5.01", "\ud83d\udcc3 Solution for Exercise M5.02", "Table of contents", "\ud83c\udfa5 Intuitions on tree-based models", "Decision tree in classification", "Hyperparameters of decision tree", "Intuitions on tree-based models", "Module overview", "Main take-away", "\u2705 Quiz M5.01", "\u2705 Quiz 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146, 149, 151, 152, 156, 158, 161, 162, 173, 184], "class": [3, 15, 25, 27, 42, 60, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 98, 99, 100, 104, 105, 106, 107, 108, 109, 110, 116, 119, 130, 131, 132, 135, 137, 138, 139, 140, 142, 145, 146, 147, 149, 150, 151, 153, 154, 156, 158, 160, 172, 184], "method": [3, 4, 6, 14, 42, 60, 73, 74, 75, 76, 80, 81, 82, 86, 95, 101, 108, 111, 115, 118, 119, 120, 126, 129, 131, 132, 134, 137, 139, 140, 141, 144, 150, 151, 153, 164, 184], "along": [3, 74, 77, 85, 87, 89, 104, 128, 133, 144, 156], "document": [3, 4, 6, 15, 60, 78, 80, 81, 82, 83, 85, 87, 89, 92, 94, 95, 99, 137, 141, 142, 143, 145, 146, 150], "via": [3, 29, 36, 77, 85, 97, 103, 107, 114, 115, 116, 118, 119, 123, 125, 139, 149, 150, 160], "docstr": [3, 144], "brows": 3, "http": [3, 6, 36, 74, 81, 95, 102, 107], "org": [3, 74, 80, 81, 82, 83, 86, 91, 97, 102, 109, 131, 132, 136, 137, 140, 141, 149, 151, 153, 156, 162, 163], "stabl": [3, 108, 110, 132, 176, 184], "html": [3, 80, 82, 83, 86, 91, 97, 102, 107, 109, 131, 132, 136, 137, 140, 141, 149, 151, 153, 156, 162, 163], "try": [3, 6, 29, 47, 74, 80, 82, 83, 86, 87, 89, 91, 97, 102, 105, 109, 110, 115, 117, 119, 129, 131, 132, 134, 135, 136, 137, 140, 141, 142, 144, 145, 146, 147, 149, 151, 153, 154, 155, 156, 162, 163, 176, 181], "adopt": [3, 131, 135], "simpl": [3, 6, 47, 74, 79, 84, 93, 98, 104, 109, 110, 118, 119, 132, 135, 137, 138, 139, 150, 156, 162, 164], "convent": [3, 74, 81, 82, 137], "limit": [3, 18, 29, 32, 34, 42, 56, 58, 61, 80, 86, 102, 103, 110, 131, 132, 137, 140, 144, 153, 157, 159, 163, 164], "minimum": [3, 117, 135, 141, 153, 159, 160, 161, 163], "number": [3, 6, 16, 17, 18, 29, 37, 42, 44, 47, 49, 53, 60, 69, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 87, 89, 93, 95, 96, 98, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 113, 114, 115, 116, 117, 119, 122, 123, 125, 126, 127, 129, 131, 132, 133, 134, 135, 137, 141, 143, 144, 146, 148, 149, 151, 152, 153, 155, 156, 160, 161, 171, 176, 178, 182, 184], "object": [3, 6, 29, 47, 58, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 102, 104, 105, 106, 119, 132, 139, 141, 142, 145, 147, 149, 150, 151, 153, 154, 155, 156, 161, 176], "must": [3, 6, 44, 108, 132, 151, 184], "implement": [3, 90, 100, 109, 110, 116, 136, 139, 142, 145, 149, 152], "furthermor": [3, 47, 102, 131, 132, 134, 139], "tri": [3, 6, 66, 105, 109, 132, 135], "consist": [3, 40, 47, 73, 77, 81, 102, 129, 134, 137, 146], "name": [3, 15, 29, 60, 74, 76, 80, 81, 82, 83, 84, 85, 86, 92, 93, 98, 99, 100, 102, 104, 105, 106, 107, 108, 110, 115, 117, 120, 123, 129, 132, 134, 135, 138, 139, 141, 149, 150, 152, 153, 180, 184], "categori": [3, 69, 73, 74, 80, 86, 87, 89, 104, 106, 119, 132, 149, 157], "e": [3, 6, 18, 29, 47, 60, 64, 66, 69, 71, 72, 73, 74, 80, 81, 82, 84, 85, 86, 89, 95, 96, 97, 100, 101, 102, 108, 115, 116, 117, 118, 125, 129, 132, 134, 135, 136, 139, 141, 144, 146, 147, 149, 153, 154, 155, 156, 176, 178, 180, 184], "g": 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80, 82, 83, 85, 100, 102, 104, 108, 116, 119, 124, 127, 132, 138, 141, 144, 158], "converg": [3, 66, 82, 85], "algorithm": [3, 13, 16, 18, 24, 35, 74, 81, 82, 85, 95, 108, 109, 113, 115, 116, 117, 119, 122, 124, 125, 127, 129, 134, 137, 139, 156, 157], "over": [3, 15, 24, 29, 39, 40, 74, 93, 95, 97, 98, 101, 103, 108, 112, 116, 118, 121, 144, 147, 149, 154, 158, 160, 169, 172], "done": [3, 6, 73, 81, 89, 116, 117, 119, 127, 129, 131, 132, 134, 150, 151, 153, 156, 160], "monitor": [3, 6, 29], "score": [3, 18, 27, 28, 29, 37, 47, 58, 60, 66, 73, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108, 113, 114, 115, 116, 117, 118, 120, 122, 123, 124, 125, 126, 127, 132, 134, 135, 137, 139, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 155, 156, 162, 174, 176, 178, 184], "jargon": [3, 81, 82], "onc": [3, 6, 18, 29, 80, 82, 90, 95, 100, 102, 110, 117, 119, 139, 141, 143, 146, 147, 148, 149, 152, 153, 154, 155, 158, 178], "quantiti": [3, 6, 85, 86, 105, 108], "size": [3, 49, 56, 58, 59, 85, 92, 93, 98, 105, 107, 109, 110, 117, 127, 131, 156, 164], "weight": [3, 29, 40, 42, 44, 47, 80, 93, 95, 98, 108, 109, 115, 128, 129, 130, 132, 133, 134, 136, 137, 138, 139, 156, 171], "dure": [3, 13, 22, 25, 29, 33, 34, 39, 40, 56, 58, 72, 73, 74, 80, 85, 87, 89, 100, 102, 105, 109, 118, 125, 132, 136, 142, 145, 149, 151, 153, 156, 159, 161, 163, 169, 170, 181, 182], "four": [3, 105, 106, 141], "never": [3, 18, 56, 74, 80, 81, 85, 109, 110, 135, 144, 149, 153, 162, 178], "seen": [3, 25, 77, 79, 81, 82, 84, 85, 102, 135, 138, 141, 149, 150, 151, 159, 163], "aspect": [3, 6, 22, 34, 77, 82, 96, 126, 132, 171], "configur": [3, 82, 114, 123, 137, 149], "learnt": [3, 58, 96, 108, 137, 156], "nearest": [3, 60, 80, 81, 82, 103, 181, 184], "neighbor": [3, 60, 78, 80, 81, 82, 83, 103, 148, 155, 181, 184], "approach": [3, 6, 14, 23, 34, 40, 47, 58, 72, 73, 85, 100, 116, 131, 136, 139, 149, 151, 153, 155, 170, 182], "polynomi": [3, 43, 53, 103, 110, 131, 137], "sai": [3, 60, 108, 135, 156], "degre": [3, 43, 47, 53, 103, 108, 110, 129, 131, 132, 134, 137], "between": [3, 6, 13, 15, 18, 22, 29, 40, 47, 53, 56, 58, 60, 74, 76, 77, 80, 82, 85, 93, 97, 98, 101, 103, 105, 106, 108, 110, 112, 115, 117, 121, 124, 125, 127, 129, 131, 132, 133, 134, 135, 137, 138, 139, 141, 144, 152, 153, 155, 157, 160, 161, 169, 184], "10": [3, 4, 18, 25, 36, 42, 47, 60, 73, 74, 77, 78, 80, 83, 85, 90, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108, 110, 113, 115, 116, 117, 118, 120, 122, 127, 129, 130, 131, 132, 134, 135, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 160, 162, 176, 178, 184], "impact": [3, 18, 44, 47, 56, 85, 87, 88, 89, 90, 96, 97, 103, 113, 117, 122, 130, 132, 144, 152, 155, 160, 178, 180, 181, 182, 184], "comput": [3, 6, 29, 47, 60, 66, 74, 77, 78, 81, 82, 83, 85, 86, 88, 90, 92, 93, 94, 96, 97, 98, 99, 100, 102, 103, 108, 109, 110, 113, 115, 116, 117, 118, 122, 124, 125, 127, 129, 131, 133, 134, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 151, 152, 153, 155, 156, 158, 161, 162, 178], "inde": [3, 18, 29, 74, 80, 81, 82, 86, 90, 93, 95, 97, 98, 101, 102, 104, 105, 106, 107, 108, 109, 110, 115, 116, 117, 118, 124, 125, 126, 127, 132, 134, 135, 136, 137, 139, 140, 141, 142, 144, 145, 146, 150, 151, 153, 156, 157, 160, 161, 162], "usual": [3, 17, 80, 82, 86, 95, 101, 103, 105, 129, 134, 141, 152, 153], "inspect": [3, 6, 29, 60, 64, 82, 91, 102, 109, 114, 117, 123, 126, 130, 131, 135, 137, 139, 140, 149, 152, 153, 156, 158, 160, 162, 184], "regard": [3, 13, 14, 22, 24, 33, 34, 39, 42, 71, 73, 82, 85, 87, 89, 92, 93, 95, 96, 98, 100, 103, 105, 106, 107, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 127, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 154, 156, 158, 159, 160, 161, 162, 163], "tune": [3, 13, 17, 26, 35, 40, 43, 44, 47, 93, 97, 98, 103, 111, 118, 119, 120, 125, 127, 131, 135, 148, 152, 154, 155, 170, 176, 181, 184], "maxim": [3, 47, 95, 97, 132, 146, 147, 148, 149, 153, 154, 155, 156, 174, 181], "involv": [3, 6, 74, 99, 102, 150], "grid": [3, 97, 105, 114, 117, 118, 123, 132, 148, 151, 153, 155, 160, 164, 174, 176, 177, 178, 182, 184], "search": [3, 47, 96, 97, 114, 116, 117, 118, 119, 120, 123, 132, 136, 147, 148, 151, 154, 155, 160, 164, 174, 176, 177, 178, 181, 182, 184], "random": [3, 11, 13, 14, 15, 17, 18, 37, 47, 51, 73, 80, 95, 99, 100, 101, 102, 105, 107, 108, 110, 112, 113, 115, 116, 118, 121, 122, 124, 125, 126, 127, 131, 137, 148, 149, 152, 155, 164, 172, 177, 178, 182], "further": [3, 39, 74, 95, 96, 131, 137, 138, 151, 160, 162], "read": [3, 6, 74, 95, 105, 108, 144, 184], "post": [3, 49, 69, 170], "machin": [3, 23, 29, 34, 36, 40, 56, 58, 64, 69, 72, 74, 80, 81, 82, 84, 85, 86, 87, 89, 91, 93, 94, 97, 98, 99, 100, 101, 102, 110, 119, 124, 125, 126, 127, 131, 132, 136, 137, 139, 140, 141, 144, 149, 153], "mooc": [3, 74, 81, 91, 92, 93, 96, 98, 102, 103, 109, 111, 112, 113, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 156, 158, 159, 160, 161, 162, 163], "refer": [3, 14, 23, 34, 40, 58, 72, 73, 74, 81, 82, 87, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 107, 109, 110, 111, 112, 113, 115, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 150, 156, 158, 159, 160, 161, 162, 163, 170, 182], "process": [3, 6, 36, 58, 66, 72, 73, 74, 86, 101, 110, 116, 124, 125, 127, 132, 146, 150, 151, 158, 181], "make": [3, 6, 24, 25, 29, 44, 47, 51, 56, 60, 61, 71, 73, 74, 77, 78, 80, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 95, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 129, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 143, 144, 146, 147, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 171], "appli": [3, 6, 15, 16, 17, 18, 24, 26, 27, 29, 39, 43, 44, 47, 49, 51, 53, 60, 61, 64, 66, 69, 73, 82, 85, 86, 91, 100, 103, 104, 105, 107, 131, 138, 140, 146, 149, 150, 152, 155, 160, 162, 171, 172, 174, 178, 180, 184], "unlabel": 3, "word": [3, 29, 136, 159, 163, 178], "equival": [3, 18, 29, 47, 73, 74, 81, 93, 98, 102, 117, 130, 135, 137, 139, 141, 155, 176], "unseen": [3, 66, 74, 100, 101, 117, 140, 156], "notion": 3, "out": [3, 6, 25, 73, 74, 77, 80, 81, 85, 94, 96, 99, 101, 102, 108, 110, 113, 114, 117, 118, 122, 123, 127, 130, 132, 135, 141, 149, 151, 158, 159, 162, 163], "ti": 3, "definit": [3, 137, 150], "distribut": [3, 6, 43, 74, 75, 76, 82, 94, 95, 99, 100, 101, 102, 103, 104, 105, 106, 107, 116, 131, 132, 139, 144, 146, 148, 151, 153, 155, 157, 161, 178], "condit": [3, 6, 108, 132, 139], "check": [3, 6, 29, 47, 60, 74, 77, 80, 81, 82, 84, 85, 86, 93, 95, 96, 98, 100, 101, 102, 103, 105, 107, 109, 110, 111, 112, 114, 115, 116, 118, 119, 120, 121, 123, 124, 125, 126, 127, 129, 132, 134, 136, 137, 138, 140, 141, 142, 145, 149, 151, 152, 156, 157, 159, 160, 161, 163, 169, 176], "wikipedia": [3, 58, 110], "articl": [3, 6, 58, 110], "finish": [3, 132], "_": [3, 74, 76, 81, 82, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 115, 120, 121, 122, 123, 125, 126, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 144, 145, 150, 152, 156, 157, 160, 161, 162, 163], "end": [3, 82, 85, 92, 93, 95, 96, 98, 99, 100, 102, 103, 105, 109, 111, 112, 113, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 154, 156, 158, 159, 160, 161, 162, 163], "thei": [3, 6, 29, 47, 56, 66, 74, 78, 80, 82, 83, 85, 86, 90, 99, 100, 103, 104, 107, 108, 110, 116, 117, 119, 131, 132, 137, 141, 144, 150, 156, 161, 163, 170, 176, 181], "avail": [3, 6, 29, 36, 49, 73, 74, 75, 76, 77, 80, 81, 91, 95, 96, 104, 106, 107, 112, 121, 125, 127, 132, 136, 138, 141, 150, 153, 176], "after": [3, 18, 28, 47, 77, 85, 102, 104, 110, 113, 116, 117, 120, 122, 136, 150, 154, 155], "been": [3, 74, 77, 80, 85, 92, 100, 102, 108, 109, 116, 122, 125, 134, 139, 150, 151, 156], "slope": [3, 29, 105, 138], "intercept": [3, 40, 128, 129, 133, 134, 135, 136, 137, 138, 139, 156], "one": [3, 6, 14, 18, 25, 37, 42, 43, 47, 58, 66, 69, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 88, 90, 93, 94, 95, 97, 98, 99, 100, 102, 105, 107, 108, 109, 115, 116, 117, 119, 126, 131, 132, 134, 136, 137, 139, 141, 142, 144, 145, 149, 150, 151, 155, 156, 157, 160, 162, 172, 184], "section": [3, 77, 80, 85, 86, 92, 93, 95, 96, 98, 102, 103, 109, 111, 112, 113, 115, 116, 117, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 140, 141, 142, 143, 144, 145, 146, 153, 156, 158, 159, 160, 161, 162, 163], "about": [3, 13, 17, 22, 24, 29, 33, 36, 39, 56, 60, 64, 71, 72, 74, 77, 81, 82, 85, 94, 96, 99, 102, 103, 104, 106, 108, 115, 118, 131, 135, 139, 140, 144, 149, 157, 161, 169, 178, 181, 184], "also": [3, 13, 22, 25, 27, 29, 33, 36, 58, 60, 74, 80, 81, 82, 85, 86, 87, 88, 89, 90, 93, 96, 97, 98, 101, 102, 103, 104, 105, 108, 109, 110, 113, 114, 116, 117, 119, 122, 123, 125, 131, 132, 135, 137, 138, 139, 140, 141, 144, 150, 151, 152, 153, 155, 156, 162], "python": [3, 6, 29, 36, 66, 71, 74, 77, 87, 89, 102, 110, 116, 132, 142, 145], "pass": [3, 18, 29, 47, 60, 66, 73, 77, 85, 86, 87, 89, 93, 97, 98, 102, 110, 138, 142, 143, 145, 146, 148, 149, 151, 155, 156, 162, 184], "anoth": [3, 6, 18, 58, 73, 74, 77, 97, 99, 101, 110, 119, 125, 126, 128, 132, 133, 137, 140, 141, 144, 160], "includ": [3, 29, 34, 36, 64, 85, 97, 104, 125, 126, 129, 132, 134, 135, 137, 150, 173], "gridsearchcv": [3, 6, 97, 118, 123, 148, 149, 151, 153, 155, 160, 176, 178, 184], "someth": [3, 90, 140, 150], "occur": [3, 85, 132], "your": [3, 6, 34, 39, 60, 73, 74, 75, 78, 79, 83, 85, 86, 87, 88, 89, 90, 93, 94, 98, 100, 111, 112, 113, 114, 117, 124, 127, 128, 129, 130, 134, 142, 143, 147, 148, 154, 158, 159, 163, 176, 178], "stick": 3, "too": [3, 6, 61, 81, 91, 97, 102, 103, 113, 116, 117, 119, 122, 131, 132, 134, 135, 149, 155, 160, 178], "so": [3, 6, 14, 29, 47, 66, 74, 76, 82, 84, 85, 87, 89, 92, 93, 98, 99, 100, 101, 102, 104, 105, 108, 109, 114, 115, 117, 123, 129, 131, 132, 134, 135, 136, 137, 141, 142, 145, 150, 151, 155, 162, 176], "up": [3, 6, 10, 35, 44, 74, 77, 80, 85, 95, 97, 99, 102, 107, 108, 125, 128, 133, 137, 138, 141, 149, 161, 164, 181], "nois": [3, 47, 53, 58, 103, 110, 115, 131, 135, 137, 140], "rather": [3, 29, 34, 77, 80, 105, 106, 139, 141, 146, 153, 155], "than": [3, 6, 18, 27, 28, 29, 34, 36, 43, 47, 53, 60, 73, 74, 77, 80, 81, 82, 83, 84, 85, 86, 89, 90, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 108, 109, 110, 116, 117, 118, 119, 122, 124, 126, 127, 128, 129, 131, 132, 133, 134, 135, 139, 140, 141, 144, 146, 148, 149, 151, 152, 153, 155, 156, 159, 160, 163, 173, 176, 178, 184], "relev": [3, 60, 80, 95, 108, 141], "pattern": [3, 23, 74, 95, 124, 127, 142, 145, 151], "tell": [3, 74, 103, 108], "great": [3, 6, 49], "poorli": [3, 89], "real": [3, 49, 74, 80, 81, 85, 86, 100, 102, 128, 133, 141, 144, 161], "world": [3, 161], "fit_predict": 3, "kneighborsclassifi": [3, 60, 78, 81, 83, 184], "decisiontreeregressor": [3, 18, 92, 96, 101, 102, 103, 110, 111, 115, 118, 119, 120, 137, 160, 161, 163, 176], "One": [3, 49, 60, 69, 73, 77, 78, 83, 85, 100, 101, 106, 108, 116, 141, 144, 151], "focu": [3, 18, 74, 80, 102, 104, 107, 109, 115, 132, 141, 144, 149, 150, 156], "were": [3, 29, 66, 77, 80, 86, 95, 102, 103, 106, 115, 124, 127, 128, 131, 133, 141, 153, 155, 160, 163], "If": [3, 6, 28, 29, 37, 42, 53, 66, 74, 77, 81, 85, 92, 93, 95, 96, 97, 98, 101, 102, 103, 107, 108, 109, 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 150, 151, 155, 156, 158, 159, 160, 161, 162, 163, 174, 184], "do": [3, 6, 17, 18, 29, 58, 66, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 89, 90, 91, 94, 97, 99, 101, 102, 103, 104, 107, 113, 116, 117, 119, 122, 124, 125, 127, 129, 131, 132, 134, 136, 137, 141, 142, 145, 149, 150, 151, 152, 161, 178, 180, 184], "1d": [3, 29, 137], "5": [3, 4, 18, 29, 60, 66, 73, 74, 76, 77, 81, 82, 83, 84, 85, 86, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 126, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 144, 146, 149, 151, 152, 153, 155, 156, 157, 160, 161, 162, 163, 164, 180, 181, 184], "someon": [3, 74], "come": [3, 6, 29, 56, 74, 80, 115, 118, 125, 128, 133, 138, 150, 151, 161], "doe": [3, 6, 18, 24, 29, 36, 47, 74, 77, 82, 85, 86, 88, 89, 90, 92, 95, 96, 97, 98, 100, 101, 105, 108, 117, 120, 131, 132, 135, 139, 140, 144, 146, 149, 153, 155, 156, 161, 178, 182], "15": [3, 4, 74, 80, 82, 85, 92, 103, 104, 114, 116, 117, 120, 123, 141, 153, 155, 156, 162, 176], "continu": [3, 6, 37, 42, 43, 64, 74, 80, 102, 104, 108, 138, 139, 141, 144, 146, 157, 160], "price": [3, 49, 73, 91, 92, 102, 104, 107, 108, 143, 144, 146], "descript": [3, 29, 49, 73, 74, 78, 83, 92, 93, 96, 98, 102, 103, 107, 109, 111, 112, 113, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 156, 158, 159, 160, 161, 162, 163, 164], "room": [3, 49, 102, 107, 108], "surfac": [3, 29], "locat": [3, 75, 76, 92, 104, 105, 107, 139, 152], "ag": [3, 74, 77, 79, 80, 81, 82, 84, 85, 86, 102, 107, 108, 132, 149, 150, 153], "mri": 3, "scan": [3, 6, 150], "want": [3, 18, 74, 75, 76, 80, 81, 85, 88, 90, 91, 92, 93, 95, 96, 98, 100, 101, 102, 103, 105, 109, 111, 112, 113, 116, 118, 119, 120, 121, 122, 125, 126, 128, 129, 130, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 149, 150, 151, 156, 157, 158, 159, 160, 161, 162, 163, 176, 178, 184], "tree": [3, 6, 10, 13, 15, 16, 17, 18, 58, 74, 82, 85, 86, 88, 90, 92, 96, 101, 102, 103, 108, 109, 110, 112, 113, 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135, 137, 139, 140, 141, 144, 149, 152, 155, 156, 157, 160, 161, 163, 173], "transform": [6, 29, 40, 43, 66, 69, 81, 82, 85, 86, 91, 94, 97, 99, 102, 103, 105, 110, 116, 119, 124, 127, 129, 131, 132, 134, 135, 137, 144, 149, 150, 151, 152, 153, 178], "often": [6, 40, 42, 44, 58, 61, 74, 85, 86, 97, 99, 110, 135, 137, 144, 146, 151, 176, 181, 184], "typic": [6, 13, 49, 71, 74, 85, 102, 107, 117, 124, 127, 131, 139, 144, 146, 151, 153, 171], "categor": [6, 37, 47, 64, 69, 71, 72, 73, 74, 75, 76, 87, 89, 104, 119, 131, 132, 139, 141, 149, 157, 164, 176], "variabl": [6, 24, 29, 36, 40, 47, 49, 58, 60, 64, 66, 68, 69, 73, 77, 78, 81, 82, 83, 87, 89, 92, 95, 96, 101, 102, 103, 104, 106, 107, 110, 117, 119, 132, 138, 139, 144, 149, 151, 153, 155, 157, 164, 176, 178, 184], "seek": [6, 34, 117, 118, 141], "suffic": [6, 117], "But": [6, 91, 98, 101, 102, 107, 131, 132, 151, 152], "larg": [6, 18, 47, 69, 86, 97, 102, 105, 107, 113, 114, 116, 117, 122, 123, 126, 127, 130, 132, 134, 135, 144, 148, 151, 152, 153, 155, 178], "detect": 6, "underfit": [6, 13, 22, 29, 33, 39, 43, 44, 51, 53, 56, 58, 59, 60, 61, 96, 115, 117, 118, 119, 131, 134, 135, 137, 140, 156, 164, 169, 171], "multipl": [6, 85, 95, 108, 110, 115, 129, 134, 137, 142, 143, 145, 146, 152, 171, 172], "hyper": [6, 18, 37, 97, 99, 151, 155, 178], "control": [6, 44, 78, 82, 83, 93, 97, 98, 103, 117, 119, 125, 130, 131, 135, 137, 147, 148, 150, 152, 154, 155, 156, 160, 171, 181], "import": [6, 13, 18, 22, 29, 36, 37, 47, 58, 60, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 167, 169, 176, 178, 180, 184], "randomsearchcv": 6, "understand": [6, 13, 22, 33, 39, 56, 78, 80, 83, 85, 95, 96, 100, 103, 128, 133, 141, 160, 161, 169, 170, 181], "suit": [6, 131, 144, 170], "intuit": [6, 10, 11, 13, 14, 22, 39, 45, 56, 71, 80, 81, 85, 105, 107, 109, 110, 115, 123, 131, 135, 136, 137, 139, 140, 156, 157, 161, 162, 164], "debug": 6, "build": [6, 18, 29, 47, 71, 73, 74, 81, 91, 93, 98, 110, 116, 126, 129, 131, 134, 149, 160, 164, 166], "combin": [6, 13, 14, 16, 42, 43, 66, 74, 80, 82, 86, 95, 97, 106, 107, 108, 109, 110, 114, 115, 116, 117, 118, 119, 123, 126, 131, 132, 134, 136, 137, 146, 147, 148, 149, 151, 153, 154, 155, 156, 171, 172, 178, 181, 182], "particularli": [6, 73, 87, 89, 117], "few": [6, 74, 75, 76, 79, 80, 84, 102, 104, 106, 107, 117, 119, 129, 131, 134, 135, 151, 157], "benefit": [6, 17, 24, 33, 82, 85, 96, 115, 118, 119, 132, 144, 158, 164], "non": [6, 20, 29, 39, 42, 43, 44, 47, 66, 69, 74, 82, 90, 93, 97, 98, 99, 104, 105, 106, 107, 108, 110, 125, 129, 132, 134, 139, 155, 156, 161, 163, 164, 170, 171, 182], "engin": [6, 40, 43, 47, 95, 105, 129, 132, 134, 164], "base": [6, 13, 14, 15, 16, 18, 29, 34, 36, 42, 49, 60, 74, 75, 76, 82, 88, 90, 95, 102, 105, 110, 115, 118, 119, 125, 126, 129, 134, 135, 137, 139, 140, 141, 146, 149, 151, 156, 157, 164, 184], "seri": [6, 92, 94, 95, 99, 101, 108, 110, 115, 135, 139, 156], "threshold": [6, 15, 27, 74, 91, 107, 156, 161, 162, 172], "variou": [6, 56, 91], "attribut": [6, 37, 74, 82, 85, 95, 97, 102, 107, 108, 110, 112, 121, 135, 136, 137, 139, 148, 149, 153, 155, 178, 184], "natur": [6, 22, 36, 81, 85, 86, 102, 105, 110, 131, 132, 153], "miss": [6, 74, 85, 95, 102, 104, 106, 107, 129, 134, 153, 184], "histgradientboostingregressor": [6, 18, 29, 116, 117, 123, 146], "classifi": [6, 15, 24, 27, 42, 69, 75, 76, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 98, 99, 100, 106, 109, 110, 119, 125, 130, 131, 135, 139, 140, 142, 145, 146, 147, 149, 150, 151, 153, 154, 156, 158, 160, 162, 170, 171, 178, 180, 184], "goto": 6, "strongli": [6, 108], "advis": [6, 108], "pointer": 6, "doc": 6, "rich": 6, "didact": [6, 36, 85, 86, 104], "improv": [6, 36, 66, 90, 91, 95, 96, 98, 99, 103, 111, 113, 117, 120, 122, 137, 144, 152, 178, 181], "compris": [6, 141], "guid": [6, 131, 139, 144, 184], "everi": [6, 74, 77, 102, 105, 108, 136, 151], "explain": [6, 18, 29, 39, 58, 86, 108, 115, 116, 122, 126, 135, 144, 149, 169], "demonstr": [6, 86, 92, 109, 115, 116, 132, 137, 160], "good": [6, 22, 25, 47, 74, 77, 79, 80, 81, 84, 85, 86, 88, 90, 100, 101, 102, 103, 106, 108, 117, 118, 119, 126, 128, 129, 132, 133, 134, 135, 136, 139, 140, 141, 149, 151, 152, 153, 155, 156, 178], "softwar": [6, 36, 81], "ask": [6, 124, 127, 128, 130, 133, 135, 141], "question": [6, 80, 88, 90, 104, 128, 129, 133, 134, 135, 141, 155], "stackoverflow": 6, "github": [6, 36, 80, 82, 83, 86, 91, 97, 102, 109, 131, 132, 136, 137, 140, 141, 149, 151, 153, 156, 162, 163], "discuss": [6, 14, 18, 36, 47, 74, 77, 81, 109, 115, 117, 118, 132, 135, 156], "driven": [6, 135], "inclus": 6, "contribut": [6, 60, 82, 95, 132, 155, 178], "other": [6, 14, 27, 29, 36, 44, 60, 73, 74, 77, 81, 82, 85, 86, 87, 89, 92, 93, 94, 98, 99, 100, 101, 105, 108, 110, 117, 125, 129, 132, 134, 136, 137, 139, 140, 144, 149, 151, 152, 153, 159, 162, 163, 178, 181, 184], "advocaci": 6, "curat": 6, "overflow": 6, "code": [6, 29, 36, 49, 69, 75, 78, 79, 87, 93, 94, 110, 111, 112, 113, 114, 116, 124, 127, 128, 129, 130, 132, 133, 137, 139, 142, 143, 147, 148, 149, 151, 152, 154, 155, 158, 159, 178], "start": [6, 36, 47, 74, 78, 79, 80, 82, 83, 84, 85, 86, 88, 90, 92, 95, 96, 99, 100, 101, 102, 105, 106, 115, 117, 118, 124, 125, 127, 129, 130, 131, 132, 134, 135, 138, 139, 141, 144, 146, 148, 150, 155, 156, 157, 158], "carpentri": 6, "resourc": [6, 36, 71, 74, 105, 117, 151], "git": 6, "lab": [6, 36], "unsupervis": [6, 49], "structur": [6, 58, 71, 74, 82, 85, 86, 95, 117, 144, 155, 156, 161, 170], "instanc": [6, 47, 64, 74, 80, 81, 82, 85, 95, 102, 104, 105, 107, 108, 111, 114, 120, 123, 124, 125, 126, 127, 129, 131, 132, 134, 136, 138, 141, 143, 144, 146, 150, 153, 174, 180, 181], "sampl": [6, 14, 15, 16, 18, 20, 29, 42, 44, 49, 59, 61, 64, 74, 75, 76, 77, 80, 81, 82, 85, 86, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 113, 115, 116, 117, 119, 122, 124, 127, 128, 131, 132, 133, 135, 137, 138, 139, 140, 141, 146, 148, 149, 151, 153, 155, 156, 157, 159, 160, 161, 163, 164, 173, 182], "supervis": [6, 49, 102, 171], "recov": [6, 18, 95, 132], "link": [6, 15, 79, 80, 84, 95, 104, 105, 106, 107, 108, 117, 124, 127, 141, 144], "drive": 6, "system": [6, 74, 95], "hand": [6, 29, 91, 95, 109, 122, 132, 134, 137, 149, 151], "nuanc": 6, "deep": [6, 90, 117, 118, 160], "better": [6, 17, 18, 28, 47, 73, 77, 81, 84, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 110, 117, 118, 119, 124, 127, 128, 132, 133, 135, 136, 146, 150, 151, 153, 155, 160, 176, 184], "gradient": [6, 10, 13, 14, 16, 17, 18, 29, 82, 86, 109, 113, 114, 118, 122, 123, 146, 149, 153, 164], "boost": [6, 13, 14, 16, 17, 18, 29, 86, 113, 114, 118, 122, 123, 149, 153, 164], "classif": [6, 15, 22, 38, 39, 41, 42, 49, 60, 64, 73, 74, 80, 81, 87, 89, 91, 93, 94, 95, 98, 99, 100, 102, 106, 109, 129, 131, 134, 138, 142, 143, 144, 145, 146, 158, 160, 161, 162, 164, 169, 170, 171, 184], "regress": [6, 18, 22, 27, 38, 39, 40, 41, 42, 43, 44, 45, 49, 53, 58, 60, 66, 73, 80, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 100, 102, 107, 110, 112, 115, 121, 124, 127, 129, 130, 134, 135, 139, 140, 141, 143, 146, 148, 150, 155, 156, 159, 160, 163, 164, 169, 170, 171, 176, 184], "nativ": [6, 74, 80, 85, 86, 102, 125, 137, 149, 151, 153], "input": [6, 24, 40, 42, 58, 73, 74, 79, 81, 82, 84, 85, 88, 89, 90, 92, 95, 98, 99, 108, 110, 128, 133, 137, 138, 139, 151, 156, 157, 171, 184], "speech": 6, "text": [6, 36, 49, 105, 131, 138], "imag": [6, 95], "voic": 6, "pretrain": 6, "human": [6, 74, 105, 131], "cost": [6, 81, 102, 105, 118, 135, 152, 153], "mainten": 6, "Not": [6, 86, 87, 89, 97], "pytorch": 6, "tensorflow": 6, "introduct": [6, 56, 71, 164], "andrea": 6, "c": [6, 15, 16, 17, 18, 24, 25, 26, 27, 28, 29, 35, 37, 42, 43, 44, 47, 49, 51, 53, 60, 61, 64, 66, 69, 73, 95, 97, 106, 130, 131, 139, 141, 150, 162, 171, 172, 173, 174, 176, 178, 180, 184], "m\u00fcller": 6, "sarah": 6, "guido": 6, "handbook": 6, "jake": 6, "van": 6, "der": 6, "pla": 6, "broader": [6, 159, 163], "statist": [6, 18, 56, 74, 76, 81, 82, 85, 88, 90, 102, 107, 109, 110, 124, 127, 141, 184], "jame": 6, "witten": 6, "hasti": 6, "tibshirani": 6, "theori": [6, 109], "concept": [6, 13, 14, 22, 23, 33, 34, 39, 40, 56, 58, 72, 95, 100, 102, 146, 169, 170, 182], "kera": 6, "aur\u00e9lien": 6, "g\u00e9ron": 6, "kaggl": 6, "particip": 6, "challeng": [6, 36, 106, 137], "team": 6, "solut": [6, 10, 11, 12, 18, 19, 30, 31, 32, 36, 38, 41, 45, 59, 63, 65, 68, 85, 101, 124, 125, 136, 140, 149, 164, 166, 175, 177, 179], "share": [6, 110], "winner": 6, "wai": [6, 71, 73, 74, 77, 80, 84, 85, 97, 98, 99, 101, 109, 110, 115, 116, 117, 124, 125, 127, 137, 141, 144, 161, 176, 184], "now": [6, 18, 29, 47, 60, 73, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 89, 92, 93, 94, 95, 96, 98, 99, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 131, 132, 133, 134, 135, 137, 138, 139, 141, 142, 145, 147, 149, 151, 152, 153, 154, 156, 159, 160, 162, 163, 176, 184], "touch": 6, "briefli": 6, "fit": [6, 14, 24, 26, 29, 39, 40, 42, 43, 44, 47, 51, 64, 66, 77, 78, 80, 83, 84, 85, 88, 90, 91, 93, 97, 98, 99, 101, 102, 103, 108, 109, 110, 112, 113, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 128, 131, 132, 133, 134, 135, 136, 137, 139, 140, 141, 144, 146, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 169, 173, 178], "wider": [6, 36, 53], "mai": [6, 25, 43, 44, 47, 49, 74, 102, 107, 117, 129, 132, 134, 135, 136, 139, 141, 146, 153, 155, 162], "fail": [6, 101, 131, 142, 145], "weak": [6, 16, 117, 135, 153], "analysi": [6, 71, 80, 93, 98, 106, 107, 132, 144, 153, 155, 156, 164, 177], "kei": [6, 8, 9, 46, 48, 50, 52, 54, 62, 66, 67, 93, 98, 100, 103, 117, 120, 123, 125, 126, 135, 149, 150, 153, 160, 165], "achiev": [6, 18, 74, 79, 82, 84, 96, 98, 106, 131, 132, 160], "reliabl": [6, 95, 139], "even": [6, 36, 39, 61, 76, 77, 81, 85, 86, 88, 89, 90, 95, 99, 101, 102, 109, 116, 117, 122, 125, 131, 132, 135, 137, 139, 140, 141, 142, 143, 145, 146, 149, 155, 181, 182], "cross": [6, 13, 18, 22, 23, 24, 25, 26, 29, 33, 34, 39, 40, 44, 47, 56, 57, 59, 60, 65, 66, 73, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 100, 101, 103, 107, 108, 114, 115, 116, 117, 118, 119, 122, 123, 124, 125, 126, 127, 129, 132, 134, 140, 141, 142, 143, 145, 146, 147, 149, 150, 151, 152, 153, 154, 155, 160, 164, 169, 174, 176, 178, 181, 182, 184], "accuraci": [6, 18, 27, 60, 66, 73, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 90, 91, 93, 95, 97, 98, 99, 100, 106, 109, 115, 125, 126, 127, 128, 133, 135, 139, 142, 145, 146, 149, 150, 151, 152, 153, 154, 155, 156, 158, 162, 184], "imperfect": [6, 108], "estim": [6, 13, 17, 22, 29, 33, 39, 44, 47, 56, 60, 66, 71, 77, 81, 82, 85, 86, 91, 97, 99, 101, 107, 108, 109, 110, 111, 113, 114, 116, 117, 118, 119, 120, 122, 123, 124, 125, 127, 129, 132, 134, 137, 143, 144, 146, 149, 150, 151, 153, 169, 176, 178, 181, 184], "actual": [6, 74, 78, 81, 83, 92, 101, 102, 103, 117, 131, 141, 144, 151], "gener": [6, 17, 18, 22, 23, 28, 29, 47, 50, 56, 58, 59, 60, 66, 77, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 104, 106, 107, 109, 110, 111, 112, 113, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 129, 132, 134, 135, 137, 139, 140, 141, 143, 144, 146, 147, 148, 149, 150, 151, 153, 154, 155, 161, 164, 167, 176, 178, 184], "As": [6, 29, 47, 74, 77, 80, 81, 82, 85, 86, 88, 90, 93, 95, 97, 98, 99, 104, 106, 107, 108, 110, 115, 116, 131, 132, 135, 139, 140, 141, 143, 144, 146, 150, 151, 152, 155, 156, 160, 163], "narrow": 6, "spend": [6, 105, 117], "increasingli": 6, "effort": [6, 105], "split": [6, 15, 18, 23, 29, 37, 42, 44, 66, 74, 77, 79, 84, 85, 86, 92, 95, 97, 100, 101, 102, 104, 108, 111, 115, 116, 117, 119, 120, 122, 124, 127, 140, 141, 144, 149, 151, 153, 156, 158, 160, 161, 162, 171, 172], "afford": 6, "trust": [6, 80, 81, 82, 83, 86, 91, 97, 102, 109, 131, 132, 135, 136, 137, 140, 141, 149, 150, 151, 153, 156, 162, 163], "think": [6, 75, 76, 101, 105, 129, 134, 135], "carefulli": [6, 95], "complet": [6, 15, 29, 36, 47, 97, 99, 124, 127, 131, 132, 148, 151, 155, 178], "futur": [6, 74, 77, 86, 91, 101, 102, 149, 162], "upon": [6, 91, 98, 99], "affect": [6, 101, 132, 135, 139, 149, 156, 161, 162], "live": [6, 132], "sure": [6, 18, 60, 80, 82, 85, 90, 114, 123], "divers": [6, 156], "demograph": [6, 118], "increas": [6, 16, 18, 28, 29, 43, 44, 47, 51, 53, 58, 76, 82, 85, 98, 102, 103, 108, 113, 116, 117, 122, 132, 135, 138, 139, 140, 149, 153, 155, 156, 158, 160, 161, 162, 174, 182], "coverag": 6, "phrase": 6, "recommend": [6, 36, 71, 74, 85], "identifi": [6, 13, 71, 86, 95, 98, 103, 141, 178], "ani": [6, 15, 18, 29, 36, 43, 73, 74, 77, 80, 82, 84, 86, 89, 90, 92, 95, 98, 100, 101, 102, 103, 105, 106, 107, 108, 110, 117, 118, 119, 123, 124, 125, 126, 127, 131, 132, 136, 139, 144, 149, 151, 152, 155, 160, 173, 184], "bia": [6, 37, 51, 56, 58, 108, 129, 131, 134, 144, 164], "acquisit": 6, "full": [6, 8, 9, 18, 36, 46, 48, 49, 50, 52, 54, 62, 66, 67, 77, 78, 82, 83, 85, 97, 102, 117, 122, 124, 126, 127, 138, 147, 151, 154, 165, 178, 181], "chain": [6, 77, 82, 86], "acquir": [6, 13, 22, 33, 39, 56, 103, 105, 169, 181], "fanci": 6, "put": [6, 33, 37, 82, 103, 108, 132, 156], "product": [6, 97, 100, 102, 129, 132, 134, 149], "routin": [6, 18, 95, 184], "debt": 6, "simpler": [6, 14, 47, 86, 135], "easier": [6, 82, 85, 92, 137, 144], "maintain": 6, "less": [6, 29, 44, 86, 95, 101, 107, 108, 109, 110, 117, 118, 126, 131, 132, 134, 135, 137, 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178], "wast": [6, 117], "bit": [6, 29, 84, 102, 103, 108, 132, 137, 142, 145, 151], "monei": 6, "annoi": 6, "otherwis": [6, 18, 80, 89, 95, 97, 119, 131, 137, 138, 160], "mostli": [6, 135, 155], "harmless": 6, "medicin": 6, "kill": 6, "logic": [6, 149, 162], "fals": [6, 16, 53, 81, 85, 88, 90, 95, 97, 101, 105, 107, 123, 125, 126, 131, 132, 134, 137, 141, 145, 148, 149, 153, 155, 156, 162, 174], "brain": 6, "tumor": 6, "sent": 6, "surgeri": 6, "veri": [6, 18, 56, 60, 74, 80, 86, 88, 90, 97, 98, 100, 103, 106, 107, 108, 110, 115, 117, 119, 123, 131, 132, 135, 137, 141, 149, 150, 151, 152, 153, 160, 178], "danger": [6, 132, 153], "mr": 6, "confirm": [6, 92, 100, 105, 107, 110, 124, 127, 131, 135, 139, 144, 151, 152], "should": [6, 18, 23, 29, 34, 35, 44, 47, 69, 73, 74, 81, 82, 86, 90, 95, 97, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 114, 115, 117, 118, 123, 124, 125, 127, 128, 132, 133, 135, 137, 140, 141, 142, 145, 146, 147, 149, 150, 151, 154, 156, 158, 160, 161, 174, 176, 178, 181, 182], "delai": 6, "life": [6, 74, 124, 127, 144], "save": [6, 184], "treatment": [6, 58], "hospit": [6, 25], "stai": [6, 40, 44, 110, 132], "overcrowd": 6, "unit": [6, 74, 80, 82, 85, 86, 102, 105, 107, 108, 109, 132, 136, 138, 144, 149, 153], "chang": [6, 28, 29, 37, 47, 82, 94, 99, 108, 109, 113, 122, 125, 132, 144, 147, 150, 154, 156, 181, 184], "inpati": 6, "chose": [6, 53, 74, 115, 132], "load": [6, 18, 64, 75, 76, 77, 78, 82, 83, 85, 86, 87, 89, 93, 96, 97, 98, 100, 101, 102, 103, 106, 107, 109, 111, 116, 117, 118, 120, 129, 130, 131, 132, 134, 135, 136, 137, 140, 141, 149, 150, 152, 153, 156, 157, 158, 159, 160, 161, 162, 163, 178, 184], "interest": [6, 49, 74, 75, 76, 92, 94, 99, 100, 102, 103, 105, 107, 110, 115, 129, 131, 134, 136, 137, 139, 141, 144, 146, 149, 150, 151, 152, 153, 155, 162, 184], "focus": [6, 14, 61, 107, 109, 132, 141, 151], "easi": [6, 73, 86, 101, 102, 105, 131, 160], "accumul": 6, "target": [6, 18, 29, 39, 40, 43, 47, 49, 58, 60, 64, 66, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 155, 156, 157, 158, 162, 176, 184], "proxi": [6, 141], "reflect": [6, 102, 110, 119], "ground": [6, 115, 141], "truth": [6, 115, 141], "polici": [6, 25], "uneven": 6, "across": [6, 42, 47, 77, 82, 92, 108, 132, 139, 153, 184], "popul": [6, 74, 102, 107, 108, 141, 155], "eg": 6, "qualiti": [6, 108, 139, 144, 151], "affair": 6, "desir": [6, 79, 84, 119, 132], "qualif": 6, "respons": 6, "women": 6, "pai": [6, 85, 109], "men": 6, "pick": [6, 29, 91, 97, 104, 105, 108, 116, 150, 181], "amplifi": 6, "inequ": 6, "mechan": [6, 29, 81, 82, 151], "die": 6, "naiv": [6, 18, 29, 74, 97, 106, 116, 119], "bad": [6, 89, 98, 127, 132, 140, 141, 178], 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"predictor": [6, 14, 15, 16, 17, 66, 81, 82, 86, 99, 103, 120, 132, 149, 156], "pure": [6, 127, 162], "benefici": [6, 33, 82, 117, 125, 151, 153, 160], "intervent": [6, 74], "brittl": 6, "interpret": [6, 17, 36, 105, 107, 108, 134, 135, 136, 137, 144, 152, 156, 162], "subject": [6, 74, 141], "caution": [6, 90, 108], "feedback": 6, "loop": [6, 97, 108, 147, 149, 151, 154, 184], "todai": 6, "ai": 6, "alloc": 6, "loan": 6, "screen": [6, 8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "job": [6, 82], "prioritis": 6, "treatement": 6, "law": [6, 29], "enforc": [6, 101, 105, 117, 132, 135], "court": 6, "fairlearn": [6, 74], "assess": [6, 25, 47, 77, 81, 94, 96, 97, 99, 103, 113, 122, 128, 132, 133, 140, 141, 144, 151, 155], "shift": [6, 82, 135], "technolog": [6, 95], "induc": [6, 108, 110, 135], "societi": 6, "though": [6, 99, 116, 143, 146], "difficult": [6, 105, 117, 136, 144], "intersect": [6, 152, 155], "No": [6, 24], "found": [6, 60, 97, 106, 107, 114, 117, 123, 132, 136, 147, 149, 151, 154, 155, 156, 160, 161, 178, 184], "short": [6, 34, 80, 107, 118, 157], "move": [6, 86, 105, 135, 152, 155], "choos": [6, 18, 36, 74, 87, 89, 102, 108, 124, 125, 127, 131, 132, 137, 139, 151, 174, 184], "revolut": 6, "fantast": [6, 127], "opportun": 6, "With": [6, 37, 89, 102, 105, 117, 125, 126, 141, 149, 153, 160], "lift": 6, "roadblock": 6, "hope": [6, 91, 131, 144], "empow": 6, "varieti": [6, 36, 77], "mindset": 6, "dream": 6, "being": [6, 42, 92, 139, 155], "adventur": 6, "navig": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "slide": [8, 9, 46, 48, 50, 52, 54, 62, 67, 131, 135, 139, 152, 155, 165], "click": [8, 9, 15, 46, 48, 50, 52, 54, 62, 67, 152, 155, 165, 178], "press": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "arrow": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "go": [8, 9, 13, 22, 29, 33, 36, 39, 46, 48, 50, 52, 54, 56, 62, 67, 71, 77, 78, 83, 85, 93, 95, 97, 98, 101, 102, 103, 105, 107, 108, 115, 118, 125, 139, 141, 146, 149, 156, 165, 169, 181], "next": [8, 9, 46, 48, 50, 52, 54, 58, 62, 67, 80, 81, 85, 91, 93, 94, 98, 99, 100, 101, 109, 115, 117, 118, 129, 131, 132, 134, 135, 137, 150, 156, 165], "previou": [8, 9, 16, 18, 22, 29, 46, 47, 48, 50, 52, 54, 60, 62, 64, 66, 67, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 94, 95, 96, 97, 99, 100, 101, 102, 103, 110, 111, 114, 115, 116, 117, 119, 120, 123, 124, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 140, 142, 145, 147, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 162, 163, 165, 176, 178, 181, 184], "p": [8, 9, 46, 48, 50, 52, 54, 62, 67, 95, 134, 142, 145, 165], "toggl": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "mode": [8, 9, 36, 46, 48, 50, 52, 54, 62, 67, 165], "adapt": [10, 100, 118, 137, 140, 144, 153, 164], "adaboost": [10, 13, 115, 164], "gbdt": [10, 109, 122, 164], "exercis": [10, 11, 12, 19, 29, 30, 31, 32, 36, 38, 41, 45, 47, 59, 63, 65, 68, 80, 85, 117, 131, 136, 137, 149, 151, 156, 164, 166, 175, 176, 177, 179], "m6": [10, 11, 12, 117, 164], "speed": [10, 29, 34, 35, 44, 105, 108, 125, 151, 153, 164], "quiz": [10, 11, 12, 19, 20, 21, 30, 31, 38, 41, 45, 55, 57, 59, 63, 65, 68, 155, 164, 166, 167, 168, 175, 177, 179], "bag": [11, 13, 15, 16, 109, 111, 117, 118, 119, 120, 164], "introductori": [11, 139, 164], "forest": [11, 13, 14, 15, 17, 18, 37, 108, 112, 113, 115, 116, 118, 121, 122, 125, 126, 164], "togeth": [13, 14, 68, 73, 81, 82, 89, 94, 95, 97, 99, 109, 115, 140, 160, 164], "ensembl": [13, 14, 17, 18, 29, 58, 86, 88, 90, 108, 109, 110, 113, 115, 116, 117, 119, 120, 121, 122, 123, 125, 126, 146, 147, 149, 151, 153, 154], "famili": [13, 14, 39, 56, 58, 74, 80, 82, 86, 118, 150], "techniqu": [13, 33, 74, 108, 131], "bootstrap": [13, 14, 18, 117, 118, 119, 120, 164], "ii": [13, 22, 77, 81, 136], "belong": [13, 18, 47, 80, 84, 85, 95, 98, 106, 139, 146, 156, 162, 176], "former": [13, 74, 82, 125, 141], "strategi": [13, 14, 18, 22, 23, 24, 25, 29, 60, 77, 79, 81, 84, 87, 89, 91, 92, 94, 95, 97, 99, 100, 101, 102, 104, 109, 110, 113, 116, 119, 122, 129, 133, 134, 136, 141, 144, 151, 181, 184], "later": [13, 18, 74, 80, 81, 91, 98, 102, 116, 134, 135, 137, 140, 141, 148, 149, 151, 155], "hyperparamet": [13, 17, 26, 40, 43, 47, 60, 93, 96, 97, 98, 103, 111, 115, 118, 120, 127, 130, 131, 132, 135, 146, 148, 155, 156, 169, 170, 177, 178, 179, 180, 181, 182], "allow": [13, 14, 29, 44, 66, 74, 77, 80, 82, 85, 93, 98, 102, 104, 107, 108, 117, 124, 127, 128, 132, 133, 137, 138, 139, 142, 145, 151, 153, 156, 160, 161, 162, 169, 178, 181, 182], "skill": [13, 22, 33, 39, 56, 71, 169, 181], "carri": [13, 22, 33, 39, 47, 56, 71, 85, 96, 107, 126, 169, 181], "basic": [13, 22, 33, 36, 39, 50, 56, 71, 100, 102, 108, 120, 144, 169, 181], "usag": [13, 22, 33, 39, 56, 80, 104, 105, 106, 107, 119, 137, 151, 169, 181], "mainli": [13, 14, 22, 33, 39, 74, 104, 156, 169], "around": [13, 22, 33, 39, 74, 80, 91, 95, 98, 101, 102, 103, 105, 108, 134, 135, 140, 141, 144, 169], "overfit": [13, 16, 18, 22, 29, 33, 39, 44, 47, 51, 53, 56, 58, 59, 60, 61, 96, 97, 101, 108, 109, 110, 113, 117, 118, 119, 122, 126, 131, 132, 134, 135, 140, 151, 155, 160, 164, 169, 171, 174], "valid": [13, 18, 22, 23, 24, 25, 26, 29, 33, 34, 39, 40, 44, 47, 56, 57, 58, 60, 65, 66, 73, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 100, 101, 107, 108, 113, 114, 115, 116, 117, 118, 119, 122, 123, 124, 125, 126, 127, 129, 132, 134, 140, 141, 142, 143, 145, 146, 147, 149, 150, 151, 152, 153, 154, 160, 161, 164, 169, 174, 176, 178, 181, 182, 184], "principl": [13, 22, 34, 141], "through": [13, 22, 29, 33, 36, 39, 56, 71, 93, 98, 103, 108, 114, 123, 132, 138, 139, 142, 145, 149, 169, 178, 181], "hour": [13, 22, 39, 56, 71, 74, 77, 79, 80, 81, 82, 84, 85, 86, 149, 150, 153, 169, 181], "saw": [14, 29, 40, 47, 80, 82, 85, 86, 102, 103, 115, 117, 132, 136, 139, 140, 142, 145, 149, 151, 152, 153, 156, 160, 161, 162, 170], "parallel": [14, 16, 115, 151, 152, 155, 178], "sequenti": [14, 16, 53, 82, 116, 117, 152, 178], "intern": [14, 44, 66, 82, 86, 97, 102, 109, 110, 113, 114, 122, 123, 132, 144, 146, 149, 151, 178], "machineri": [14, 109, 115], "art": 14, "learn": [14, 16, 17, 23, 24, 27, 28, 29, 34, 37, 38, 40, 42, 51, 58, 60, 64, 66, 68, 69, 72, 74, 77, 78, 80, 82, 83, 84, 85, 86, 87, 89, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 106, 107, 109, 112, 114, 116, 117, 119, 121, 123, 124, 125, 126, 127, 130, 131, 132, 134, 137, 139, 140, 141, 142, 144, 145, 146, 149, 151, 152, 154, 157, 161, 162, 170, 171, 172, 173, 179, 180, 182, 184], "earli": [14, 29, 113, 114, 122, 123], "stop": [14, 29, 87, 89, 92, 99, 105, 113, 114, 117, 122, 123], "stack": 14, "By": [15, 18, 29, 47, 74, 82, 85, 97, 100, 103, 105, 119, 127, 132, 141, 144, 146, 159, 163, 176], "default": [15, 18, 27, 28, 47, 60, 66, 77, 78, 82, 83, 85, 93, 98, 100, 105, 111, 117, 118, 120, 130, 132, 141, 142, 143, 144, 145, 146, 150, 151, 176, 181], "baggingclassifi": [15, 119], "baggingregressor": [15, 110, 111, 118, 119, 120], "draw": [15, 29, 74, 94, 99, 110, 117, 125, 153, 156, 178], "replac": 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"overfit/bias_vs_variance_quiz_m2_03.md", "overfit/bias_vs_variance_slides.md", "overfit/learning_validation_curves_quiz_m2_02.md", "overfit/learning_validation_curves_slides.md", "overfit/overfit_bias_variance_index.md", "overfit/overfit_module_intro.md", "overfit/overfit_overfitting_underfitting_index.md", "overfit/overfit_take_away.md", "overfit/overfit_validation_learning_curves_index.md", "overfit/overfit_wrap_up_quiz.md", "overfit/overfitting_vs_under_fitting_quiz_m2_01.md", "overfit/overfitting_vs_under_fitting_slides.md", "predictive_modeling_pipeline/01_tabular_data_exploration_index.md", "predictive_modeling_pipeline/01_tabular_data_exploration_quiz_m1_01.md", "predictive_modeling_pipeline/02_numerical_pipeline_index.md", "predictive_modeling_pipeline/02_numerical_pipeline_quiz_m1_02.md", "predictive_modeling_pipeline/02_numerical_pipeline_video_cross_validation.md", "predictive_modeling_pipeline/03_categorical_pipeline_index.md", "predictive_modeling_pipeline/03_categorical_pipeline_quiz_m1_03.md", "predictive_modeling_pipeline/03_categorical_pipeline_visualization_video.md", "predictive_modeling_pipeline/predictive_modeling_module_intro.md", "predictive_modeling_pipeline/predictive_modeling_module_take_away.md", "predictive_modeling_pipeline/wrap_up_quiz.md", "python_scripts/01_tabular_data_exploration.py", "python_scripts/01_tabular_data_exploration_ex_01.py", "python_scripts/01_tabular_data_exploration_sol_01.py", "python_scripts/02_numerical_pipeline_cross_validation.py", "python_scripts/02_numerical_pipeline_ex_00.py", "python_scripts/02_numerical_pipeline_ex_01.py", "python_scripts/02_numerical_pipeline_hands_on.py", "python_scripts/02_numerical_pipeline_introduction.py", "python_scripts/02_numerical_pipeline_scaling.py", "python_scripts/02_numerical_pipeline_sol_00.py", "python_scripts/02_numerical_pipeline_sol_01.py", "python_scripts/03_categorical_pipeline.py", "python_scripts/03_categorical_pipeline_column_transformer.py", "python_scripts/03_categorical_pipeline_ex_01.py", "python_scripts/03_categorical_pipeline_ex_02.py", "python_scripts/03_categorical_pipeline_sol_01.py", "python_scripts/03_categorical_pipeline_sol_02.py", "python_scripts/03_categorical_pipeline_visualization.py", "python_scripts/cross_validation_baseline.py", "python_scripts/cross_validation_ex_01.py", "python_scripts/cross_validation_ex_02.py", "python_scripts/cross_validation_grouping.py", "python_scripts/cross_validation_learning_curve.py", "python_scripts/cross_validation_nested.py", "python_scripts/cross_validation_sol_01.py", "python_scripts/cross_validation_sol_02.py", "python_scripts/cross_validation_stratification.py", "python_scripts/cross_validation_time.py", "python_scripts/cross_validation_train_test.py", "python_scripts/cross_validation_validation_curve.py", "python_scripts/datasets_ames_housing.py", "python_scripts/datasets_bike_rides.py", 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"python_scripts/linear_models_ex_02.py", "python_scripts/linear_models_ex_03.py", "python_scripts/linear_models_feature_engineering_classification.py", "python_scripts/linear_models_regularization.py", "python_scripts/linear_models_sol_01.py", "python_scripts/linear_models_sol_02.py", "python_scripts/linear_models_sol_03.py", "python_scripts/linear_regression_in_sklearn.py", "python_scripts/linear_regression_non_linear_link.py", "python_scripts/linear_regression_without_sklearn.py", "python_scripts/logistic_regression.py", "python_scripts/logistic_regression_non_linear.py", "python_scripts/metrics_classification.py", "python_scripts/metrics_ex_01.py", "python_scripts/metrics_ex_02.py", "python_scripts/metrics_regression.py", "python_scripts/metrics_sol_01.py", "python_scripts/metrics_sol_02.py", "python_scripts/parameter_tuning_ex_02.py", "python_scripts/parameter_tuning_ex_03.py", "python_scripts/parameter_tuning_grid_search.py", "python_scripts/parameter_tuning_manual.py", "python_scripts/parameter_tuning_nested.py", "python_scripts/parameter_tuning_parallel_plot.py", "python_scripts/parameter_tuning_randomized_search.py", "python_scripts/parameter_tuning_sol_02.py", "python_scripts/parameter_tuning_sol_03.py", "python_scripts/trees_classification.py", "python_scripts/trees_dataset.py", "python_scripts/trees_ex_01.py", "python_scripts/trees_ex_02.py", "python_scripts/trees_hyperparameters.py", "python_scripts/trees_regression.py", "python_scripts/trees_sol_01.py", "python_scripts/trees_sol_02.py", "toc.md", "trees/slides.md", "trees/trees_classification_index.md", "trees/trees_hyperparameters_index.md", "trees/trees_intuitions_index.md", "trees/trees_module_intro.md", "trees/trees_module_take_away.md", "trees/trees_quiz_m5_01.md", "trees/trees_quiz_m5_02.md", "trees/trees_quiz_m5_03.md", "trees/trees_quiz_m5_04.md", "trees/trees_regression_index.md", "trees/trees_wrap_up_quiz.md", "tuning/parameter_tuning_automated_index.md", "tuning/parameter_tuning_automated_quiz_m3_02.md", "tuning/parameter_tuning_manual_index.md", "tuning/parameter_tuning_manual_quiz_m3_01.md", "tuning/parameter_tuning_module_intro.md", "tuning/parameter_tuning_module_take_away.md", "tuning/parameter_tuning_parallel_plot_video.md", "tuning/parameter_tuning_wrap_up_quiz.md"], "titles": ["Acknowledgement", "The adult census dataset", "Datasets description", "Glossary", "Notebook timings", "Table of contents", "Concluding remarks", "\ud83c\udfa5 Concluding remarks", "\ud83c\udfa5 Intuitions on ensemble models: bagging", "\ud83c\udfa5 Intuitions on ensemble models: boosting", "Ensemble based on boosting", "Ensemble method using bootstrapping", "Hyperparameter tuning with ensemble methods", "Module overview", "Main take-away", "\u2705 Quiz M6.01", "\u2705 Quiz M6.02", "\u2705 Quiz M6.03", "\ud83c\udfc1 Wrap-up quiz 6", "Comparing a model with simple baselines", "Choice of cross-validation", "Nested cross-validation", "Module overview", "Main take-away", "\u2705 Quiz M7.01", "\u2705 Quiz M7.02", "\u2705 Quiz M7.03", "\u2705 Quiz M7.04", "\u2705 Quiz M7.05", "\ud83c\udfc1 Wrap-up quiz 7", "Classification metrics", "Regression metrics", "Caveats of feature selection", "Module overview", "Main take-away", "\u2705 Quiz", "Introduction", "\u2705 Quiz", "Intuitions on linear models", "Module overview", "Main take-away", "Non-linear feature engineering for linear models", "\u2705 Quiz M4.01", "\u2705 Quiz M4.02", "\u2705 Quiz M4.03", "Regularization in linear model", "\ud83c\udfa5 Intuitions on linear models", "\ud83c\udfc1 Wrap-up quiz 4", "\ud83c\udfa5 Intuitions on regularized linear models", "\u2705 Quiz Intro.01", "\ud83c\udfa5 Introducing machine-learning concepts", "\u2705 Quiz M2.03", "\ud83c\udfa5 Bias versus Variance", "\u2705 Quiz M2.02", "\ud83c\udfa5 Comparing train and test errors", "Bias versus variance trade-off", "Module overview", "Overfitting and underfitting", "Main take-away", "Validation and learning curves", "\ud83c\udfc1 Wrap-up quiz 2", "\u2705 Quiz M2.01", "\ud83c\udfa5 Overfitting and Underfitting", "Tabular data exploration", "\u2705 Quiz M1.01", "Fitting a scikit-learn model on numerical data", "\u2705 Quiz M1.02", "\ud83c\udfa5 Validation of a model", "Handling categorical data", "\u2705 Quiz M1.03", "\ud83c\udfa5 Visualizing scikit-learn pipelines in Jupyter", "Module overview", "Main take-away", "\ud83c\udfc1 Wrap-up quiz 1", "First look at our dataset", "\ud83d\udcdd Exercise M1.01", "\ud83d\udcc3 Solution for Exercise M1.01", "Model evaluation using cross-validation", "\ud83d\udcdd Exercise M1.02", "\ud83d\udcdd Exercise M1.03", "Working with numerical data", "First model with scikit-learn", "Preprocessing for numerical features", "\ud83d\udcc3 Solution for Exercise M1.02", "\ud83d\udcc3 Solution for Exercise M1.03", "Encoding of categorical variables", "Using numerical and categorical variables together", "\ud83d\udcdd Exercise M1.04", "\ud83d\udcdd Exercise M1.05", "\ud83d\udcc3 Solution for Exercise M1.04", "\ud83d\udcc3 Solution for Exercise M1.05", "Visualizing scikit-learn pipelines in Jupyter", "Comparing model performance with a simple baseline", "\ud83d\udcdd Exercise M2.01", "\ud83d\udcdd Exercise M7.01", "Sample grouping", "Effect of the sample size in cross-validation", "Nested cross-validation", "\ud83d\udcc3 Solution for Exercise M2.01", "\ud83d\udcc3 Solution for Exercise M7.01", "Stratification", "Non i.i.d. data", "Cross-validation framework", "Overfit-generalization-underfit", "The Ames housing dataset", "The bike rides dataset", "The blood transfusion dataset", "The California housing dataset", "Feature importance", "Adaptive Boosting (AdaBoost)", "Bagging", "\ud83d\udcdd Exercise M6.01", "\ud83d\udcdd Exercise M6.02", "\ud83d\udcdd Exercise M6.03", "\ud83d\udcdd Exercise M6.04", "Gradient-boosting decision tree (GBDT)", "Speeding-up gradient-boosting", "Hyperparameter tuning", "Introductory example to ensemble models", "Random forests", "\ud83d\udcc3 Solution for Exercise M6.01", "\ud83d\udcc3 Solution for Exercise M6.02", "\ud83d\udcc3 Solution for Exercise M6.03", "\ud83d\udcc3 Solution for Exercise M6.04", "\ud83d\udcdd Exercise 01", "Benefits of using feature selection", "Limitation of selecting feature using a model", "\ud83d\udcc3 Solution for Exercise 01", "\ud83d\udcdd Exercise M4.01", "\ud83d\udcdd Exercise M4.02", "\ud83d\udcdd Exercise M4.03", "Non-linear feature engineering for Logistic Regression", "Regularization of linear regression model", "\ud83d\udcc3 Solution for Exercise M4.01", "\ud83d\udcc3 Solution for Exercise M4.02", "\ud83d\udcc3 Solution for Exercise M4.03", "Linear regression using scikit-learn", "Non-linear feature engineering for Linear Regression", "Linear regression without scikit-learn", "Linear models for classification", "Beyond linear separation in classification", "Classification", "\ud83d\udcdd Exercise M7.02", "\ud83d\udcdd Exercise M7.03", "Regression", "\ud83d\udcc3 Solution for Exercise M7.02", "\ud83d\udcc3 Solution for Exercise M7.03", "\ud83d\udcdd Exercise M3.01", "\ud83d\udcdd Exercise M3.02", "Hyperparameter tuning by grid-search", "Set and get hyperparameters in scikit-learn", "Evaluation and hyperparameter tuning", "Analysis of hyperparameter search results", "Hyperparameter tuning by randomized-search", "\ud83d\udcc3 Solution for Exercise M3.01", "\ud83d\udcc3 Solution for Exercise M3.02", "Build a classification decision tree", "The penguins datasets", "\ud83d\udcdd Exercise M5.01", "\ud83d\udcdd Exercise M5.02", "Importance of decision tree hyperparameters on generalization", "Decision tree for regression", "\ud83d\udcc3 Solution for Exercise M5.01", "\ud83d\udcc3 Solution for Exercise M5.02", "Table of contents", "\ud83c\udfa5 Intuitions on tree-based models", "Decision tree in classification", "Hyperparameters of decision tree", "Intuitions on tree-based models", "Module overview", "Main take-away", "\u2705 Quiz M5.01", "\u2705 Quiz 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80, 81, 83, 85, 86, 87, 89, 93, 95, 98, 105, 107, 108, 115, 117, 118, 131, 137, 139, 141, 142, 145, 146, 149, 157, 169, 170, 184], "n_sampl": [3, 110, 115, 125, 126, 131, 137, 139, 140, 160], "row": [3, 18, 60, 64, 74, 85, 91, 102, 104, 107, 110, 129, 132, 134, 137, 149, 152, 153, 155, 184], "n_featur": [3, 43, 117, 119, 125, 126, 131, 137, 139, 140], "column": [3, 6, 18, 29, 37, 42, 43, 47, 60, 64, 69, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 91, 93, 94, 98, 99, 101, 102, 104, 105, 106, 107, 108, 110, 115, 117, 119, 120, 121, 123, 125, 126, 129, 131, 132, 134, 137, 138, 140, 141, 142, 143, 144, 145, 146, 147, 149, 151, 152, 153, 154, 155, 160, 161, 163, 176, 184], "equal": [3, 6, 29, 42, 44, 60, 82, 100, 115, 132, 149, 150, 161], "flower": 3, "4": [3, 4, 18, 29, 73, 74, 76, 80, 81, 82, 83, 84, 85, 91, 92, 95, 98, 99, 100, 102, 104, 105, 106, 107, 108, 110, 115, 117, 120, 123, 125, 129, 131, 132, 134, 135, 137, 138, 141, 146, 149, 150, 151, 152, 153, 155, 157, 160, 162, 164, 176], "length": [3, 6, 18, 29, 75, 76, 80, 81, 104, 105, 109, 112, 121, 128, 129, 130, 133, 134, 135, 136, 138, 139, 141, 149, 153, 156, 157, 158, 159, 160, 161, 162, 163, 184], "width": [3, 116], "common": [3, 58, 77, 85, 101, 110, 132, 144, 162, 184], "math": [3, 104], "matric": [3, 85], "capit": [3, 74, 77, 79, 80, 81, 82, 84, 85, 86, 149, 150, 153], "letter": [3, 102, 107], "f": [3, 8, 9, 18, 29, 46, 48, 50, 52, 54, 62, 67, 74, 77, 80, 81, 82, 83, 84, 85, 86, 88, 89, 90, 91, 95, 97, 98, 100, 101, 102, 107, 108, 109, 110, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 132, 133, 134, 135, 136, 137, 138, 139, 141, 144, 145, 146, 149, 150, 151, 153, 154, 156, 160, 162, 165, 178], "iter": [3, 29, 66, 77, 82, 85, 100, 101, 102, 113, 117, 122, 132, 151, 152, 153], "optim": [3, 22, 23, 44, 64, 82, 93, 97, 98, 109, 114, 115, 116, 118, 123, 127, 129, 132, 134, 136, 141, 144, 146, 149, 150, 151, 153, 155, 160, 176, 182, 184], "befor": [3, 44, 47, 74, 78, 80, 82, 83, 85, 100, 102, 104, 108, 116, 119, 124, 127, 132, 138, 141, 144, 158], "converg": [3, 66, 82, 85], "algorithm": [3, 13, 16, 18, 24, 35, 74, 81, 82, 85, 95, 108, 109, 113, 115, 116, 117, 119, 122, 124, 125, 127, 129, 134, 137, 139, 156, 157], "over": [3, 15, 24, 29, 39, 40, 74, 93, 95, 97, 98, 101, 103, 108, 112, 116, 118, 121, 144, 147, 149, 154, 158, 160, 169, 172], "done": [3, 6, 73, 81, 89, 116, 117, 119, 127, 129, 131, 132, 134, 150, 151, 153, 156, 160], "monitor": [3, 6, 29], "score": [3, 18, 27, 28, 29, 37, 47, 58, 60, 66, 73, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108, 113, 114, 115, 116, 117, 118, 120, 122, 123, 124, 125, 126, 127, 132, 134, 135, 137, 139, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 155, 156, 162, 174, 176, 178, 184], "jargon": [3, 81, 82], "onc": [3, 6, 18, 29, 80, 82, 90, 95, 100, 102, 110, 117, 119, 139, 141, 143, 146, 147, 148, 149, 152, 153, 154, 155, 158, 178], "quantiti": [3, 6, 85, 86, 105, 108], "size": [3, 49, 56, 58, 59, 85, 92, 93, 98, 105, 107, 109, 110, 117, 127, 131, 156, 164], "weight": [3, 29, 40, 42, 44, 47, 80, 93, 95, 98, 108, 109, 115, 128, 129, 130, 132, 133, 134, 136, 137, 138, 139, 156, 171], "dure": [3, 13, 22, 25, 29, 33, 34, 39, 40, 56, 58, 72, 73, 74, 80, 85, 87, 89, 100, 102, 105, 109, 118, 125, 132, 136, 142, 145, 149, 151, 153, 156, 159, 161, 163, 169, 170, 181, 182], "four": [3, 105, 106, 141], "never": [3, 18, 56, 74, 80, 81, 85, 109, 110, 135, 144, 149, 153, 162, 178], "seen": [3, 25, 77, 79, 81, 82, 84, 85, 102, 135, 138, 141, 149, 150, 151, 159, 163], "aspect": [3, 6, 22, 34, 77, 82, 96, 126, 132, 171], "configur": [3, 82, 114, 123, 137, 149], "learnt": [3, 58, 96, 108, 137, 156], "nearest": [3, 60, 80, 81, 82, 103, 181, 184], "neighbor": [3, 60, 78, 80, 81, 82, 83, 103, 148, 155, 181, 184], "approach": [3, 6, 14, 23, 34, 40, 47, 58, 72, 73, 85, 100, 116, 131, 136, 139, 149, 151, 153, 155, 170, 182], "polynomi": [3, 43, 53, 103, 110, 131, 137], "sai": [3, 60, 108, 135, 156], "degre": [3, 43, 47, 53, 103, 108, 110, 129, 131, 132, 134, 137], "between": [3, 6, 13, 15, 18, 22, 29, 40, 47, 53, 56, 58, 60, 74, 76, 77, 80, 82, 85, 93, 97, 98, 101, 103, 105, 106, 108, 110, 112, 115, 117, 121, 124, 125, 127, 129, 131, 132, 133, 134, 135, 137, 138, 139, 141, 144, 152, 153, 155, 157, 160, 161, 169, 184], "10": [3, 4, 18, 25, 36, 42, 47, 60, 73, 74, 77, 78, 80, 83, 85, 90, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108, 110, 113, 115, 116, 117, 118, 120, 122, 127, 129, 130, 131, 132, 134, 135, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 160, 162, 176, 178, 184], "impact": [3, 18, 44, 47, 56, 85, 87, 88, 89, 90, 96, 97, 103, 113, 117, 122, 130, 132, 144, 152, 155, 160, 178, 180, 181, 182, 184], "comput": [3, 6, 29, 47, 60, 66, 74, 77, 78, 81, 82, 83, 85, 86, 88, 90, 92, 93, 94, 96, 97, 98, 99, 100, 102, 103, 108, 109, 110, 113, 115, 116, 117, 118, 122, 124, 125, 127, 129, 131, 133, 134, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 151, 152, 153, 155, 156, 158, 161, 162, 178], "inde": [3, 18, 29, 74, 80, 81, 82, 86, 90, 93, 95, 97, 98, 101, 102, 104, 105, 106, 107, 108, 109, 110, 115, 116, 117, 118, 124, 125, 126, 127, 131, 132, 134, 135, 136, 137, 139, 140, 141, 142, 144, 145, 146, 150, 151, 153, 156, 157, 160, 161, 162], "usual": [3, 17, 80, 82, 86, 95, 101, 103, 105, 129, 134, 141, 152, 153], "inspect": [3, 6, 29, 60, 64, 82, 91, 102, 109, 114, 117, 123, 126, 130, 131, 135, 137, 139, 140, 149, 152, 153, 156, 158, 160, 162, 184], "regard": [3, 13, 14, 22, 24, 33, 34, 39, 42, 71, 73, 82, 85, 87, 89, 92, 93, 95, 96, 98, 100, 103, 105, 106, 107, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 127, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 154, 156, 158, 159, 160, 161, 162, 163], "tune": [3, 13, 17, 26, 35, 40, 43, 44, 47, 93, 97, 98, 103, 111, 118, 119, 120, 125, 127, 131, 135, 148, 152, 154, 155, 170, 176, 181, 184], "maxim": [3, 47, 95, 97, 132, 146, 147, 148, 149, 153, 154, 155, 156, 174, 181], "involv": [3, 6, 74, 99, 102, 150], "grid": [3, 97, 105, 114, 117, 118, 123, 132, 148, 151, 153, 155, 160, 164, 174, 176, 177, 178, 182, 184], "search": [3, 47, 96, 97, 114, 116, 117, 118, 119, 120, 123, 132, 136, 147, 148, 151, 154, 155, 160, 164, 174, 176, 177, 178, 181, 182, 184], "random": [3, 11, 13, 14, 15, 17, 18, 37, 47, 51, 73, 80, 95, 99, 100, 101, 102, 105, 107, 108, 110, 112, 113, 115, 116, 118, 121, 122, 124, 125, 126, 127, 131, 137, 148, 149, 152, 155, 164, 172, 177, 178, 182], "further": [3, 39, 74, 95, 96, 131, 137, 138, 151, 160, 162], "read": [3, 6, 74, 95, 105, 108, 144, 184], "post": [3, 49, 69, 170], "machin": [3, 23, 29, 34, 36, 40, 56, 58, 64, 69, 72, 74, 80, 81, 82, 84, 85, 86, 87, 89, 91, 93, 94, 97, 98, 99, 100, 101, 102, 110, 119, 124, 125, 126, 127, 131, 132, 136, 137, 139, 140, 141, 144, 149, 153], "mooc": [3, 74, 81, 91, 92, 93, 96, 98, 102, 103, 109, 111, 112, 113, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 156, 158, 159, 160, 161, 162, 163], "refer": [3, 14, 23, 34, 40, 58, 72, 73, 74, 81, 82, 87, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 107, 109, 110, 111, 112, 113, 115, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 150, 156, 158, 159, 160, 161, 162, 163, 170, 182], "process": [3, 6, 36, 58, 66, 72, 73, 74, 86, 101, 110, 116, 124, 125, 127, 132, 146, 150, 151, 158, 181], "make": [3, 6, 24, 25, 29, 44, 47, 51, 56, 60, 61, 71, 73, 74, 77, 78, 80, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 95, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 129, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 143, 144, 146, 147, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 171], "appli": [3, 6, 15, 16, 17, 18, 24, 26, 27, 29, 39, 43, 44, 47, 49, 51, 53, 60, 61, 64, 66, 69, 73, 82, 85, 86, 91, 100, 103, 104, 105, 107, 131, 138, 140, 146, 149, 150, 152, 155, 160, 162, 171, 172, 174, 178, 180, 184], "unlabel": 3, "word": [3, 29, 136, 159, 163, 178], "equival": [3, 18, 29, 47, 73, 74, 81, 93, 98, 102, 117, 130, 135, 137, 139, 141, 155, 176], "unseen": [3, 66, 74, 100, 101, 117, 140, 156], "notion": 3, "out": [3, 6, 25, 73, 74, 77, 80, 81, 85, 94, 96, 99, 101, 102, 108, 110, 113, 114, 117, 118, 122, 123, 127, 130, 132, 135, 141, 149, 151, 158, 159, 162, 163], "ti": 3, "definit": [3, 137, 150], "distribut": [3, 6, 43, 74, 75, 76, 82, 94, 95, 99, 100, 101, 102, 103, 104, 105, 106, 107, 116, 131, 132, 139, 144, 146, 148, 151, 153, 155, 157, 161, 178], "condit": [3, 6, 108, 132, 139], "check": [3, 6, 29, 47, 60, 74, 77, 80, 81, 82, 84, 85, 86, 93, 95, 96, 98, 100, 101, 102, 103, 105, 107, 109, 110, 111, 112, 114, 115, 116, 118, 119, 120, 121, 123, 124, 125, 126, 127, 129, 132, 134, 136, 137, 138, 140, 141, 142, 145, 149, 151, 152, 156, 157, 159, 160, 161, 163, 169, 176], "wikipedia": [3, 58, 110], "articl": [3, 6, 58, 110], "finish": [3, 132], "_": [3, 74, 76, 81, 82, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 115, 120, 121, 122, 123, 125, 126, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 144, 145, 150, 152, 156, 157, 160, 161, 162, 163], "end": [3, 82, 85, 92, 93, 95, 96, 98, 99, 100, 102, 103, 105, 109, 111, 112, 113, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 154, 156, 158, 159, 160, 161, 162, 163], "thei": [3, 6, 29, 47, 56, 66, 74, 78, 80, 82, 83, 85, 86, 90, 99, 100, 103, 104, 107, 108, 110, 116, 117, 119, 131, 132, 137, 141, 144, 150, 156, 161, 163, 170, 176, 181], "avail": [3, 6, 29, 36, 49, 73, 74, 75, 76, 77, 80, 81, 91, 95, 96, 104, 106, 107, 112, 121, 125, 127, 132, 136, 138, 141, 150, 153, 176], "after": [3, 18, 28, 47, 77, 85, 102, 104, 110, 113, 116, 117, 120, 122, 136, 150, 154, 155], "been": [3, 74, 77, 80, 85, 92, 100, 102, 108, 109, 116, 122, 125, 134, 139, 150, 151, 156], "slope": [3, 29, 105, 138], "intercept": [3, 40, 128, 129, 133, 134, 135, 136, 137, 138, 139, 156], "one": [3, 6, 14, 18, 25, 37, 42, 43, 47, 58, 66, 69, 73, 74, 76, 77, 79, 80, 81, 82, 84, 85, 86, 88, 90, 93, 94, 95, 97, 98, 99, 100, 102, 105, 107, 108, 109, 115, 116, 117, 119, 126, 131, 132, 134, 136, 137, 139, 141, 142, 144, 145, 149, 150, 151, 155, 156, 157, 160, 162, 172, 184], "section": [3, 77, 80, 85, 86, 92, 93, 95, 96, 98, 102, 103, 109, 111, 112, 113, 115, 116, 117, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 140, 141, 142, 143, 144, 145, 146, 153, 156, 158, 159, 160, 161, 162, 163], "about": [3, 13, 17, 22, 24, 29, 33, 36, 39, 56, 60, 64, 71, 72, 74, 77, 81, 82, 85, 94, 96, 99, 102, 103, 104, 106, 108, 115, 118, 131, 135, 139, 140, 144, 149, 157, 161, 169, 178, 181, 184], "also": [3, 13, 22, 25, 27, 29, 33, 36, 58, 60, 74, 80, 81, 82, 85, 86, 87, 88, 89, 90, 93, 96, 97, 98, 101, 102, 103, 104, 105, 108, 109, 110, 113, 114, 116, 117, 119, 122, 123, 125, 131, 132, 135, 137, 138, 139, 140, 141, 144, 150, 151, 152, 153, 155, 156, 162], "python": [3, 6, 29, 36, 66, 71, 74, 77, 87, 89, 102, 110, 116, 132, 142, 145], "pass": [3, 18, 29, 47, 60, 66, 73, 77, 85, 86, 87, 89, 93, 97, 98, 102, 110, 138, 142, 143, 145, 146, 148, 149, 151, 155, 156, 162, 184], "anoth": [3, 6, 18, 58, 73, 74, 77, 97, 99, 101, 110, 119, 125, 126, 128, 132, 133, 137, 140, 141, 144, 160], "includ": [3, 29, 34, 36, 64, 85, 97, 104, 125, 126, 129, 132, 134, 135, 137, 150, 173], "gridsearchcv": [3, 6, 97, 118, 123, 148, 149, 151, 153, 155, 160, 176, 178, 184], "someth": [3, 90, 140, 150], "occur": [3, 85, 132], "your": [3, 6, 34, 39, 60, 73, 74, 75, 78, 79, 83, 85, 86, 87, 88, 89, 90, 93, 94, 98, 100, 111, 112, 113, 114, 117, 124, 127, 128, 129, 130, 134, 142, 143, 147, 148, 154, 158, 159, 163, 176, 178], "stick": 3, "too": [3, 6, 61, 81, 91, 97, 102, 103, 113, 116, 117, 119, 122, 131, 132, 134, 135, 149, 155, 160, 178], "so": [3, 6, 14, 29, 47, 66, 74, 76, 82, 84, 85, 87, 89, 92, 93, 98, 99, 100, 101, 102, 104, 105, 108, 109, 114, 115, 117, 123, 129, 131, 132, 134, 135, 136, 137, 141, 142, 145, 150, 151, 155, 162, 176], "up": [3, 6, 10, 35, 44, 74, 77, 80, 85, 95, 97, 99, 102, 107, 108, 125, 128, 133, 137, 138, 141, 149, 161, 164, 181], "nois": [3, 47, 53, 58, 103, 110, 115, 131, 135, 137, 140], "rather": [3, 29, 34, 77, 80, 105, 106, 139, 141, 146, 153, 155], "than": [3, 6, 18, 27, 28, 29, 34, 36, 43, 47, 53, 60, 73, 74, 77, 80, 81, 82, 83, 84, 85, 86, 89, 90, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 108, 109, 110, 116, 117, 118, 119, 122, 124, 126, 127, 128, 129, 131, 132, 133, 134, 135, 139, 140, 141, 144, 146, 148, 149, 151, 152, 153, 155, 156, 159, 160, 163, 173, 176, 178, 184], "relev": [3, 60, 80, 95, 108, 141], "pattern": [3, 23, 74, 95, 124, 127, 142, 145, 151], "tell": [3, 74, 103, 108], "great": [3, 6, 49], "poorli": [3, 89], "real": [3, 49, 74, 80, 81, 85, 86, 100, 102, 128, 133, 141, 144, 161], "world": [3, 161], "fit_predict": 3, "kneighborsclassifi": [3, 60, 78, 81, 83, 184], "decisiontreeregressor": [3, 18, 92, 96, 101, 102, 103, 110, 111, 115, 118, 119, 120, 137, 160, 161, 163, 176], "One": [3, 49, 60, 69, 73, 77, 78, 83, 85, 100, 101, 106, 108, 116, 141, 144, 151], "focu": [3, 18, 74, 80, 102, 104, 107, 109, 115, 132, 141, 144, 149, 150, 156], "were": [3, 29, 66, 77, 80, 86, 95, 102, 103, 106, 115, 124, 127, 128, 131, 133, 141, 153, 155, 160, 163], "If": [3, 6, 28, 29, 37, 42, 53, 66, 74, 77, 81, 85, 92, 93, 95, 96, 97, 98, 101, 102, 103, 107, 108, 109, 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 150, 151, 155, 156, 158, 159, 160, 161, 162, 163, 174, 184], "do": [3, 6, 17, 18, 29, 58, 66, 74, 75, 76, 77, 78, 79, 80, 82, 83, 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116, 117, 122, 132, 135, 138, 139, 140, 149, 153, 155, 156, 158, 160, 161, 162, 174, 182], "coverag": 6, "phrase": 6, "recommend": [6, 36, 71, 74, 85], "identifi": [6, 13, 71, 86, 95, 98, 103, 141, 178], "ani": [6, 15, 18, 29, 36, 43, 73, 74, 77, 80, 82, 84, 86, 89, 90, 92, 95, 98, 100, 101, 102, 103, 105, 106, 107, 108, 110, 117, 118, 119, 123, 124, 125, 126, 127, 131, 132, 136, 139, 144, 149, 151, 152, 155, 160, 173, 184], "bia": [6, 37, 51, 56, 58, 108, 129, 131, 134, 144, 164], "acquisit": 6, "full": [6, 8, 9, 18, 36, 46, 48, 49, 50, 52, 54, 62, 66, 67, 77, 78, 82, 83, 85, 97, 102, 117, 122, 124, 126, 127, 138, 147, 151, 154, 165, 178, 181], "chain": [6, 77, 82, 86], "acquir": [6, 13, 22, 33, 39, 56, 103, 105, 169, 181], "fanci": 6, "put": [6, 33, 37, 82, 103, 108, 132, 156], "product": [6, 97, 100, 102, 129, 132, 134, 149], "routin": [6, 18, 95, 184], "debt": 6, "simpler": [6, 14, 47, 86, 135], "easier": [6, 82, 85, 92, 137, 144], "maintain": 6, "less": [6, 29, 44, 86, 95, 101, 107, 108, 109, 110, 117, 118, 126, 131, 132, 134, 135, 137, 149, 155], "power": [6, 29, 36, 105, 117, 118, 129, 132, 134, 137, 156], "drift": 6, "gave": [6, 141], "methodolog": [6, 36, 56, 102, 161], "element": [6, 22, 81, 85, 95, 102, 132, 133, 138, 141], "alwai": [6, 15, 18, 22, 24, 42, 44, 53, 79, 80, 81, 84, 86, 90, 92, 95, 97, 98, 99, 100, 102, 103, 106, 107, 119, 125, 140, 141, 150, 151, 152, 163, 176, 178, 184], "solid": 6, "conclus": [6, 74, 94, 95, 97, 99, 100, 101, 103, 106, 117, 125], "standpoint": 6, "biggest": 6, "shortcom": 6, "cannot": [6, 18, 29, 58, 74, 100, 101, 103, 108, 117, 124, 127, 131, 137, 140, 141, 144, 146, 149, 152, 163, 181], "autom": [6, 74, 164, 182], "domain": 6, "knowledg": [6, 36, 71, 97, 103, 110, 127, 137, 140, 151], "critic": [6, 36, 104], "thing": [6, 74, 85, 86, 91, 101, 137, 149], "oper": [6, 82, 110, 131, 141, 151], "risk": [6, 129, 134, 140], "advertis": 6, "individu": [6, 17, 29, 74, 82, 86, 106, 110, 112, 119, 121, 132, 139, 140, 156, 184], "caus": [6, 18, 51, 58, 74, 85, 89, 90, 117, 132, 149, 151, 178], "wast": [6, 117], "bit": [6, 29, 84, 102, 103, 108, 132, 137, 142, 145, 151], "monei": 6, "annoi": 6, "otherwis": [6, 18, 80, 89, 95, 97, 119, 131, 137, 138, 160], "mostli": [6, 135, 155], "harmless": 6, "medicin": 6, "kill": 6, "logic": [6, 149, 162], "fals": [6, 16, 53, 81, 85, 88, 90, 95, 97, 101, 105, 107, 123, 125, 126, 131, 132, 134, 137, 141, 145, 148, 149, 153, 155, 156, 162, 174], "brain": 6, "tumor": 6, "sent": 6, "surgeri": 6, "veri": [6, 18, 56, 60, 74, 80, 86, 88, 90, 97, 98, 100, 103, 106, 107, 108, 110, 115, 117, 119, 123, 131, 132, 135, 137, 141, 149, 150, 151, 152, 153, 160, 178], "danger": [6, 132, 153], "mr": 6, "confirm": [6, 92, 100, 105, 107, 110, 124, 127, 131, 135, 139, 144, 151, 152], "should": [6, 18, 23, 29, 34, 35, 44, 47, 69, 73, 74, 81, 82, 86, 90, 95, 97, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 114, 115, 117, 118, 123, 124, 125, 127, 128, 132, 133, 135, 137, 140, 141, 142, 145, 146, 147, 149, 150, 151, 154, 156, 158, 160, 161, 174, 176, 178, 181, 182], "delai": 6, "life": [6, 74, 124, 127, 144], "save": [6, 184], "treatment": [6, 58], "hospit": [6, 25], "stai": [6, 40, 44, 110, 132], "overcrowd": 6, "unit": [6, 74, 80, 82, 85, 86, 102, 105, 107, 108, 109, 132, 136, 138, 144, 149, 153], "chang": [6, 28, 29, 37, 47, 82, 94, 99, 108, 109, 113, 122, 125, 132, 144, 147, 150, 154, 156, 181, 184], "inpati": 6, "chose": [6, 53, 74, 115, 132], "load": [6, 18, 64, 75, 76, 77, 78, 82, 83, 85, 86, 87, 89, 93, 96, 97, 98, 100, 101, 102, 103, 106, 107, 109, 111, 116, 117, 118, 120, 129, 130, 131, 132, 134, 135, 136, 137, 140, 141, 149, 150, 152, 153, 156, 157, 158, 159, 160, 161, 162, 163, 178, 184], "interest": [6, 49, 74, 75, 76, 92, 94, 99, 100, 102, 103, 105, 107, 110, 115, 129, 131, 134, 136, 137, 139, 141, 144, 146, 149, 150, 151, 152, 153, 155, 162, 184], "focus": [6, 14, 61, 107, 109, 132, 141, 151], "easi": [6, 73, 86, 101, 102, 105, 131, 160], "accumul": 6, "target": [6, 18, 29, 39, 40, 43, 47, 49, 58, 60, 64, 66, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 155, 156, 157, 158, 162, 176, 184], "proxi": [6, 141], "reflect": [6, 102, 110, 119], "ground": [6, 115, 141], "truth": [6, 115, 141], "polici": [6, 25], "uneven": 6, "across": [6, 42, 47, 77, 82, 92, 108, 132, 139, 153, 184], "popul": [6, 74, 102, 107, 108, 141, 155], "eg": 6, "qualiti": [6, 108, 139, 144, 151], "affair": 6, "desir": [6, 79, 84, 119, 132], "qualif": 6, "respons": 6, "women": 6, "pai": [6, 85, 109], "men": 6, "pick": [6, 29, 91, 97, 104, 105, 108, 116, 150, 181], "amplifi": 6, "inequ": 6, "mechan": [6, 29, 81, 82, 151], "die": 6, "naiv": [6, 18, 29, 74, 97, 106, 116, 119], "bad": [6, 89, 98, 127, 132, 140, 141, 178], "health": [6, 36], "fallaci": 6, "compar": [6, 13, 17, 18, 22, 29, 37, 47, 49, 59, 60, 73, 74, 77, 79, 81, 82, 84, 85, 86, 87, 89, 97, 98, 99, 103, 107, 108, 110, 113, 115, 116, 120, 122, 125, 126, 127, 129, 132, 134, 141, 144, 153, 155, 156, 164, 176, 184], "wors": [6, 18, 28, 47, 73, 98, 99, 101, 136, 176], "baselin": [6, 22, 24, 79, 84, 86, 87, 89, 94, 95, 98, 99, 118, 164], "heart": [6, 29, 105, 129, 134], "pressur": 6, "greater": [6, 29, 73, 131], "trigger": 6, "care": [6, 34, 47, 85, 97, 100, 106, 107, 124, 127, 132], "which": [6, 14, 16, 17, 18, 23, 25, 27, 29, 33, 34, 40, 42, 44, 47, 58, 66, 69, 72, 73, 74, 77, 80, 81, 82, 84, 85, 86, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108, 109, 110, 115, 116, 117, 119, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 144, 145, 146, 148, 149, 150, 152, 153, 155, 156, 160, 161, 162, 169, 170, 171, 176, 178, 180, 182, 184], "learner": [6, 14, 109, 110, 115, 117, 118], "predictor": [6, 14, 15, 16, 17, 66, 81, 82, 86, 99, 103, 120, 132, 149, 156], "pure": [6, 127, 162], "benefici": [6, 33, 82, 117, 125, 151, 153, 160], "intervent": [6, 74], "brittl": 6, "interpret": [6, 17, 36, 105, 107, 108, 134, 135, 136, 137, 144, 152, 156, 162], "subject": [6, 74, 141], "caution": [6, 90, 108], "feedback": 6, "loop": [6, 97, 108, 147, 149, 151, 154, 184], "todai": 6, "ai": 6, "alloc": 6, "loan": 6, "screen": [6, 8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "job": [6, 82], "prioritis": 6, "treatement": 6, "law": [6, 29], "enforc": [6, 101, 105, 117, 132, 135], "court": 6, "fairlearn": [6, 74], "assess": [6, 25, 47, 77, 81, 94, 96, 97, 99, 103, 113, 122, 128, 132, 133, 140, 141, 144, 151, 155], "shift": [6, 82, 135], "technolog": [6, 95], "induc": [6, 108, 110, 135], "societi": 6, "though": [6, 99, 116, 143, 146], "difficult": [6, 105, 117, 136, 144], "intersect": [6, 152, 155], "No": [6, 24], "found": [6, 60, 97, 106, 107, 114, 117, 123, 132, 136, 147, 149, 151, 154, 155, 156, 160, 161, 178, 184], "short": [6, 34, 80, 107, 118, 157], "move": [6, 86, 105, 135, 152, 155], "choos": [6, 18, 36, 74, 87, 89, 102, 108, 124, 125, 127, 131, 132, 137, 139, 151, 174, 184], "revolut": 6, "fantast": [6, 127], "opportun": 6, "With": [6, 37, 89, 102, 105, 117, 125, 126, 141, 149, 153, 160], "lift": 6, "roadblock": 6, "hope": [6, 91, 131, 144], "empow": 6, "varieti": [6, 36, 77], "mindset": 6, "dream": 6, "being": [6, 42, 92, 139, 155], "adventur": 6, "navig": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "slide": [8, 9, 46, 48, 50, 52, 54, 62, 67, 131, 135, 139, 152, 155, 165], "click": [8, 9, 15, 46, 48, 50, 52, 54, 62, 67, 152, 155, 165, 178], "press": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "arrow": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "go": [8, 9, 13, 22, 29, 33, 36, 39, 46, 48, 50, 52, 54, 56, 62, 67, 71, 77, 78, 83, 85, 93, 95, 97, 98, 101, 102, 103, 105, 107, 108, 115, 118, 125, 139, 141, 146, 149, 156, 165, 169, 181], "next": [8, 9, 46, 48, 50, 52, 54, 58, 62, 67, 80, 81, 85, 91, 93, 94, 98, 99, 100, 101, 109, 115, 117, 118, 129, 131, 132, 134, 135, 137, 150, 156, 165], "previou": [8, 9, 16, 18, 22, 29, 46, 47, 48, 50, 52, 54, 60, 62, 64, 66, 67, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 94, 95, 96, 97, 99, 100, 101, 102, 103, 110, 111, 114, 115, 116, 117, 119, 120, 123, 124, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 140, 142, 145, 147, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 162, 163, 165, 176, 178, 181, 184], "p": [8, 9, 46, 48, 50, 52, 54, 62, 67, 95, 134, 142, 145, 165], "toggl": [8, 9, 46, 48, 50, 52, 54, 62, 67, 165], "mode": [8, 9, 36, 46, 48, 50, 52, 54, 62, 67, 165], "adapt": [10, 100, 118, 137, 140, 144, 153, 164], "adaboost": [10, 13, 115, 164], "gbdt": [10, 109, 122, 164], "exercis": [10, 11, 12, 19, 29, 30, 31, 32, 36, 38, 41, 45, 47, 59, 63, 65, 68, 80, 85, 117, 131, 136, 137, 149, 151, 156, 164, 166, 175, 176, 177, 179], "m6": [10, 11, 12, 117, 164], "speed": [10, 29, 34, 35, 44, 105, 108, 125, 151, 153, 164], "quiz": [10, 11, 12, 19, 20, 21, 30, 31, 38, 41, 45, 55, 57, 59, 63, 65, 68, 155, 164, 166, 167, 168, 175, 177, 179], "bag": [11, 13, 15, 16, 109, 111, 117, 118, 119, 120, 164], "introductori": [11, 139, 164], "forest": [11, 13, 14, 15, 17, 18, 37, 108, 112, 113, 115, 116, 118, 121, 122, 125, 126, 164], "togeth": [13, 14, 68, 73, 81, 82, 89, 94, 95, 97, 99, 109, 115, 140, 160, 164], "ensembl": [13, 14, 17, 18, 29, 58, 86, 88, 90, 108, 109, 110, 113, 115, 116, 117, 119, 120, 121, 122, 123, 125, 126, 146, 147, 149, 151, 153, 154], "famili": [13, 14, 39, 56, 58, 74, 80, 82, 86, 118, 150], "techniqu": [13, 33, 74, 108, 131], "bootstrap": [13, 14, 18, 117, 118, 119, 120, 164], "ii": [13, 22, 77, 81, 136], "belong": [13, 18, 47, 80, 84, 85, 95, 98, 106, 139, 146, 156, 162, 176], "former": [13, 74, 82, 125, 141], "strategi": [13, 14, 18, 22, 23, 24, 25, 29, 60, 77, 79, 81, 84, 87, 89, 91, 92, 94, 95, 97, 99, 100, 101, 102, 104, 109, 110, 113, 116, 119, 122, 129, 133, 134, 136, 141, 144, 151, 181, 184], "later": [13, 18, 74, 80, 81, 91, 98, 102, 116, 134, 135, 137, 140, 141, 148, 149, 151, 155], "hyperparamet": [13, 17, 26, 40, 43, 47, 60, 93, 96, 97, 98, 103, 111, 115, 118, 120, 127, 130, 131, 132, 135, 146, 148, 155, 156, 169, 170, 177, 178, 179, 180, 181, 182], "allow": [13, 14, 29, 44, 66, 74, 77, 80, 82, 85, 93, 98, 102, 104, 107, 108, 117, 124, 127, 128, 132, 133, 137, 138, 139, 142, 145, 151, 153, 156, 160, 161, 162, 169, 178, 181, 182], "skill": [13, 22, 33, 39, 56, 71, 169, 181], "carri": [13, 22, 33, 39, 47, 56, 71, 85, 96, 107, 126, 169, 181], "basic": [13, 22, 33, 36, 39, 50, 56, 71, 100, 102, 108, 120, 144, 169, 181], "usag": [13, 22, 33, 39, 56, 80, 104, 105, 106, 107, 119, 137, 151, 169, 181], "mainli": [13, 14, 22, 33, 39, 74, 104, 156, 169], "around": [13, 22, 33, 39, 74, 80, 91, 95, 98, 101, 102, 103, 105, 108, 134, 135, 140, 141, 144, 169], "overfit": [13, 16, 18, 22, 29, 33, 39, 44, 47, 51, 53, 56, 58, 59, 60, 61, 96, 97, 101, 108, 109, 110, 113, 117, 118, 119, 122, 126, 131, 132, 134, 135, 140, 151, 155, 160, 164, 169, 171, 174], "valid": [13, 18, 22, 23, 24, 25, 26, 29, 33, 34, 39, 40, 44, 47, 56, 57, 58, 60, 65, 66, 73, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 100, 101, 107, 108, 113, 114, 115, 116, 117, 118, 119, 122, 123, 124, 125, 126, 127, 129, 132, 134, 140, 141, 142, 143, 145, 146, 147, 149, 150, 151, 152, 153, 154, 160, 161, 164, 169, 174, 176, 178, 181, 182, 184], "principl": [13, 22, 34, 141], "through": [13, 22, 29, 33, 36, 39, 56, 71, 93, 98, 103, 108, 114, 123, 132, 138, 139, 142, 145, 149, 169, 178, 181], "hour": [13, 22, 39, 56, 71, 74, 77, 79, 80, 81, 82, 84, 85, 86, 149, 150, 153, 169, 181], "saw": [14, 29, 40, 47, 80, 82, 85, 86, 102, 103, 115, 117, 132, 136, 139, 140, 142, 145, 149, 151, 152, 153, 156, 160, 161, 162, 170], "parallel": [14, 16, 115, 151, 152, 155, 178], "sequenti": [14, 16, 53, 82, 116, 117, 152, 178], "intern": [14, 44, 66, 82, 86, 97, 102, 109, 110, 113, 114, 122, 123, 132, 144, 146, 149, 151, 178], "machineri": [14, 109, 115], "art": 14, "learn": [14, 16, 17, 23, 24, 27, 28, 29, 34, 37, 38, 40, 42, 51, 58, 60, 64, 66, 68, 69, 72, 74, 77, 78, 80, 82, 83, 84, 85, 86, 87, 89, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 106, 107, 109, 112, 114, 116, 117, 119, 121, 123, 124, 125, 126, 127, 130, 131, 132, 134, 137, 139, 140, 141, 142, 144, 145, 146, 149, 151, 152, 154, 157, 161, 162, 170, 171, 172, 173, 179, 180, 182, 184], "earli": [14, 29, 113, 114, 122, 123], "stop": [14, 29, 87, 89, 92, 99, 105, 113, 114, 117, 122, 123], "stack": 14, "By": [15, 18, 29, 47, 74, 82, 85, 97, 100, 103, 105, 119, 127, 132, 141, 144, 146, 159, 163, 176], "default": [15, 18, 27, 28, 47, 60, 66, 77, 78, 82, 83, 85, 93, 98, 100, 105, 111, 117, 118, 120, 130, 131, 132, 141, 142, 143, 144, 145, 146, 150, 151, 176, 181], "baggingclassifi": [15, 119], "baggingregressor": [15, 110, 111, 118, 119, 120], "draw": [15, 29, 74, 94, 99, 110, 117, 125, 153, 156, 178], "replac": [15, 29, 47, 85, 104, 105, 107, 110, 131], "without": [15, 36, 38, 47, 60, 74, 78, 81, 83, 92, 95, 97, 98, 101, 102, 104, 105, 106, 117, 118, 125, 126, 129, 132, 134, 135, 136, 140, 141, 142, 144, 145, 162, 164, 184], "d": [15, 16, 17, 18, 20, 25, 27, 29, 37, 42, 43, 44, 47, 49, 51, 53, 60, 61, 64, 66, 69, 73, 74, 81, 95, 164, 171, 176, 178, 180, 184], "answer": [15, 16, 17, 18, 24, 25, 26, 27, 28, 29, 35, 42, 43, 44, 47, 49, 51, 53, 60, 61, 64, 66, 69, 73, 85, 104, 129, 134, 135, 155, 171, 172, 173, 174, 176, 178, 180, 184], "hint": [15, 28, 29, 47, 60, 73, 75, 76, 78, 79, 83, 84, 88, 90, 129, 132, 134, 135, 162, 184], "base_estim": [15, 120], "decid": [15, 22, 74, 80, 102, 107, 124, 127, 129, 134, 151], "resampl": [15, 51, 73, 97, 105, 109, 132], "perform": [15, 17, 18, 19, 22, 23, 24, 26, 29, 34, 35, 47, 60, 64, 66, 73, 76, 77, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 106, 108, 109, 111, 112, 113, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 129, 132, 134, 137, 140, 141, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 160, 169, 176, 178, 181, 182, 184], "correct": [16, 17, 18, 29, 60, 81, 83, 109, 115, 117, 129, 134, 141, 147, 153, 154, 160, 184], "statement": [16, 18, 27, 29, 42, 73, 178, 184], "simultan": 16, "histogram": [16, 29, 66, 74, 75, 76, 82, 94, 99, 104, 107, 114, 116, 123, 139, 149, 153], "acceler": [16, 29, 105, 116], "subsampl": [16, 107, 119, 124, 127], "origin": [16, 66, 77, 80, 81, 85, 86, 100, 101, 104, 109, 110, 115, 116, 119, 129, 131, 134, 135, 137, 138, 144, 156, 157, 161, 162], "bin": [16, 74, 82, 92, 95, 99, 102, 103, 104, 105, 106, 107, 116, 131, 137, 152, 153], "numer": [16, 47, 64, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 83, 84, 85, 94, 99, 102, 104, 105, 107, 129, 131, 132, 134, 137, 148, 149, 150, 155, 164, 176, 184], "tend": [16, 29, 95, 117, 118, 131, 132, 135, 144, 151], "true": [16, 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150, 151, 153, 154, 155, 156, 158, 160, 161, 162, 163, 176, 178, 180, 184], "randomforestregressor": [18, 108, 115, 117, 119, 121, 122], "except": [18, 87, 89, 142, 145, 156, 162], "exact": [18, 29, 115], "fold": [18, 25, 29, 47, 60, 73, 77, 95, 100, 102, 108, 114, 123, 125, 126, 127, 128, 129, 132, 133, 134, 143, 146, 151, 153, 155, 176, 184], "model_select": [18, 29, 47, 60, 77, 79, 80, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 107, 108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 130, 132, 134, 135, 139, 141, 142, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 155, 156, 158, 160, 162, 176, 178, 184], "cross_valid": [18, 29, 47, 60, 66, 73, 77, 85, 86, 87, 88, 89, 90, 91, 92, 93, 98, 99, 100, 103, 107, 108, 114, 115, 116, 118, 123, 125, 126, 132, 134, 142, 143, 145, 146, 149, 150, 151, 176, 184], "cv": [18, 29, 47, 66, 73, 77, 86, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 108, 114, 118, 123, 126, 132, 134, 142, 145, 146, 149, 151, 153, 154, 160, 176, 178, 184], "store": [18, 29, 74, 80, 82, 92, 94, 99, 100, 102, 104, 105, 106, 110, 118, 125, 126, 131, 132, 136, 140, 148, 149, 155, 184], "return_train_scor": [18, 29, 60, 103, 132], "count": [18, 47, 60, 73, 74, 76, 80, 82, 84, 85, 92, 95, 100, 104, 105, 106, 107, 156, 176, 184], "rang": [18, 40, 47, 60, 66, 73, 80, 82, 95, 97, 100, 102, 105, 106, 107, 108, 110, 131, 132, 138, 151, 152, 153, 155, 157, 159, 162, 163, 173, 176, 178, 184], "substanti": [18, 176, 184], "almost": [18, 47, 73, 81, 90, 102, 108, 135, 138, 144, 151, 162, 176], "100": [18, 47, 60, 80, 81, 92, 96, 99, 102, 103, 107, 108, 110, 111, 113, 114, 115, 116, 117, 118, 120, 122, 123, 124, 125, 126, 127, 129, 131, 132, 134, 135, 137, 140, 141, 144, 148, 153, 154, 155, 162, 184], "again": [18, 93, 98, 108, 110, 113, 122, 135, 137, 141, 152], "curv": [18, 42, 58, 60, 93, 98, 113, 122, 134, 138, 139, 141, 164], "n_estim": [18, 109, 110, 113, 115, 116, 117, 118, 119, 120, 121, 122], "numpi": [18, 29, 36, 60, 64, 66, 71, 82, 92, 95, 96, 98, 99, 100, 103, 105, 107, 108, 109, 110, 115, 116, 121, 122, 124, 125, 127, 128, 131, 132, 133, 134, 136, 137, 138, 140, 141, 143, 144, 146, 152, 155, 160, 161, 162, 163, 178], "np": [18, 29, 47, 60, 92, 93, 95, 96, 98, 99, 100, 103, 105, 107, 108, 109, 110, 113, 115, 116, 119, 121, 122, 124, 125, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 140, 141, 143, 144, 146, 148, 152, 155, 160, 161, 162, 163, 173, 178], "arrai": [18, 29, 42, 43, 60, 66, 77, 80, 81, 82, 83, 85, 86, 95, 96, 102, 103, 108, 109, 110, 113, 116, 121, 122, 129, 132, 134, 139, 141, 145, 149, 156], "200": [18, 60, 73, 95, 105, 115, 116, 131, 132, 153], "500": [18, 60, 85, 86, 89, 102, 105, 107, 144, 152, 153], "1_000": [18, 29, 95, 113, 122], "decreas": [18, 29, 44, 47, 53, 58, 82, 108, 116, 125, 132, 144, 149, 155], "becom": [18, 93, 96, 98, 103, 108, 116, 117, 149, 153, 162, 178], "reach": [18, 96, 103, 113, 122, 149, 152, 160, 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149, 150, 152, 153, 178], "83120264": 18, "83309064": 18, "83195043": 18, "84834224": 18, "85790323": 18, "86235297": 18, "84791111": 18, "85183089": 18, "82241954": 18, "85045978": 18, "perfect": [18, 42, 56, 92, 102, 108, 115, 124, 126, 127, 140, 141, 160], "r2": [18, 28, 101, 107, 118, 123, 144, 146], "surpris": [18, 84, 95, 100, 101, 124, 127, 156, 162], "memor": [18, 81, 101, 102, 103], "expect": [18, 29, 66, 74, 76, 80, 85, 86, 97, 100, 101, 102, 107, 110, 117, 127, 129, 132, 134, 140, 144, 146, 151, 155, 160], "automat": [18, 66, 74, 80, 82, 85, 87, 89, 102, 150, 151, 176, 181], "prevent": [18, 43, 85, 89, 110, 117, 178], "max_it": [18, 29, 82, 85, 86, 89, 95, 116, 117, 123, 135], "recal": [18, 27, 60, 85, 97, 102, 116, 118, 135, 141, 150, 151, 153, 155, 184], "averag": [18, 29, 60, 81, 92, 95, 99, 100, 102, 105, 107, 108, 110, 115, 116, 117, 118, 119, 122, 125, 132, 135, 136, 141, 142, 144, 145, 146, 184], "small": [18, 40, 73, 76, 77, 86, 95, 98, 100, 102, 103, 108, 110, 115, 116, 117, 129, 132, 134, 135, 137, 147, 149, 154, 178], "behav": [18, 93, 98, 99, 132, 135, 176], "high": [18, 29, 37, 51, 53, 58, 64, 74, 76, 80, 84, 85, 98, 103, 104, 105, 106, 107, 108, 131, 135, 139, 149, 150, 153], "optimum": 18, "m7": [19, 20, 21, 30, 31, 164], "stratif": [20, 164], "framework": [22, 23, 34, 56, 57, 96, 100, 103, 114, 123, 143, 146, 151, 164, 181], "keep": [22, 29, 74, 91, 95, 102, 104, 105, 107, 108, 125, 126, 127, 129, 130, 134, 135, 137, 139, 140, 151, 153], "mind": [22, 74, 102, 108, 117, 124, 125, 126, 127, 140, 153], "metric": [22, 27, 29, 66, 77, 81, 96, 101, 102, 106, 110, 111, 113, 120, 121, 122, 125, 129, 132, 133, 134, 136, 137, 142, 143, 144, 145, 146, 152, 161, 164, 184], "besid": [22, 23, 33, 39, 80, 86, 88, 90, 92, 95, 96, 114, 123, 125, 156, 169, 182], "insight": [22, 33, 36, 47, 64, 74, 101, 103, 110, 124, 127, 138, 139, 144, 153], "addit": [22, 29, 43, 69, 77, 81, 85, 102, 105, 107, 113, 114, 118, 119, 122, 123, 132, 134, 137, 138, 141, 144, 149, 150, 151, 153, 155, 176], "necess": [22, 102], "appropri": [22, 74], "nest": [22, 23, 26, 117, 132, 147, 149, 151, 154, 164, 176, 182, 184], "wise": [23, 131, 151, 152, 182], "encount": [23, 73, 85, 87, 89, 100], "show": [23, 39, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 86, 91, 96, 97, 100, 102, 103, 104, 107, 109, 110, 115, 117, 118, 119, 126, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 141, 142, 145, 149, 150, 151, 152, 153, 155, 156, 158, 161, 162, 163, 169, 178, 181], "comparison": [23, 40, 97, 118, 141], "remov": [24, 35, 47, 74, 108, 116, 127, 129, 134, 149, 152, 184], "dummi": [24, 60, 79, 84, 89, 92, 94, 99, 141, 144], "reli": [24, 74, 80, 82, 108, 109, 126, 141], "ye": [24, 42, 43], "whatev": [24, 152], "chosen": [24, 85, 89, 106, 113, 122, 144, 182], "record": [25, 29, 49, 74, 81, 85, 102, 105, 106, 108], "suppos": [25, 85, 95], "imbalanc": [25, 60, 74, 99, 132, 141, 184], "addition": [25, 82, 132], "suspect": 25, "systemat": [25, 51, 58, 73, 98, 144, 150], 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"stronger": [27, 105, 110, 135, 155], "lead": [27, 58, 74, 85, 86, 89, 95, 101, 115, 117, 118, 119, 132, 134, 135, 141, 148, 151, 152, 153, 155], "lower": [27, 28, 29, 47, 53, 89, 95, 96, 99, 102, 105, 108, 117, 122, 132, 134, 135, 141, 144, 146, 151, 155, 162], "r": [28, 98, 101, 102, 107, 108, 143, 144, 146], "absolut": [28, 29, 47, 92, 96, 102, 103, 111, 112, 113, 115, 116, 120, 121, 122, 129, 133, 134, 136, 137, 143, 144, 146], "median": [28, 91, 92, 97, 102, 107, 108, 118, 123, 125, 126, 132, 144, 145, 172, 173], "cross_val_scor": [28, 60, 95, 97, 101, 102, 119, 127, 142, 143, 145, 146, 147, 154], "model_a": 28, "neg_mean_squared_error": [28, 132, 146], "strictli": 28, "model_b": 28, "rememb": [28, 47, 74, 110, 129, 134, 135, 180, 184], "alia": 28, "neg": [28, 29, 47, 66, 102, 103, 108, 132, 133, 138, 141], "guarante": [28, 108, 160], "either": [28, 69, 81, 95, 98, 100, 124, 125, 127, 133, 139, 141, 144], "open": [29, 47, 60, 73, 78, 80, 83, 101, 104, 105, 176], "bike_rid": [29, 105], "command": [29, 47, 60, 73, 176, 184], "cycl": [29, 105], "index_col": [29, 101, 105, 152, 153, 178], "parse_d": [29, 101, 105], "index": [29, 47, 74, 81, 95, 100, 101, 105, 108, 110, 116, 125, 129, 132, 134, 135, 139, 145, 149, 151, 156], "appendix": [29, 92, 93, 96, 98, 102, 103, 109, 111, 112, 113, 116, 118, 119, 120, 121, 122, 128, 129, 130, 132, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 156, 158, 159, 160, 161, 162, 163], "remind": 29, "cheap": [29, 49, 86], "sensor": [29, 74, 105], "gp": [29, 105], "cyclist": [29, 105], "meter": [29, 105], "expens": [29, 73, 116, 149, 182], "blindli": 29, "introduc": [29, 36, 81, 82, 102, 108, 110, 125, 129, 132, 134, 137, 138, 144, 146, 164], "flavor": 29, "classic": 29, "newton": 29, "second": [29, 37, 73, 82, 85, 88, 90, 94, 99, 100, 105, 109, 115, 116, 131, 140, 144, 153, 160, 162], "p_": 29, "meca": 29, "rho": 29, "sc_x": 29, "v_": 29, "c_r": 29, "mg": 29, "co": 29, "alpha": [29, 44, 47, 74, 95, 98, 107, 108, 109, 110, 115, 121, 131, 132, 133, 135, 136, 137, 138, 139, 140, 144, 156, 160, 161, 162, 163], "sin": 29, "ma": 29, "v_d": 29, "air": 29, "densiti": [29, 131], "kg": [29, 157], "m": [29, 85, 95], "frontal": 29, "c_x": 29, "drag": 29, "coeffici": [29, 37, 40, 43, 47, 107, 132, 134, 135, 137, 138, 139, 144, 171], "v_a": 29, "roll": 29, "rider": 29, "bicycl": 29, "standard": [29, 47, 74, 77, 82, 86, 87, 89, 94, 95, 97, 99, 102, 103, 108, 114, 123, 132, 140, 141, 151], "graviti": 29, "81": [29, 73, 79, 84, 116, 117], "radian": 29, "equat": [29, 136, 139], "complex": [29, 44, 61, 80, 86, 87, 89, 91, 96, 104, 115, 135, 142, 145, 160, 161], "term": [29, 74, 81, 82, 101, 102, 110, 112, 115, 116, 119, 121, 125, 138, 156, 161], "within": [29, 34, 77, 95, 97, 100, 102, 107, 108, 110, 114, 116, 123, 125, 131, 132, 143, 146, 151, 152, 156, 163], "parenthesi": 29, "produc": [29, 162], "fight": [29, 39], "wind": 29, "resist": 29, "tire": 29, "floor": 29, "third": [29, 115, 131, 134], "hill": 29, "forward": [29, 105], "fourth": 29, "last": [29, 36, 47, 69, 85, 100, 104, 106, 131, 132, 137, 139, 141, 142, 145], "hi": [29, 106], "simplifi": [29, 47, 101, 102, 125, 139, 155, 157, 176], "beta_": 29, "closer": [29, 44, 100, 105, 132, 135, 139], "previous": [29, 47, 73, 85, 86, 93, 98, 101, 104, 109, 115, 116, 117, 118, 129, 131, 134, 135, 137, 139, 144, 147, 149, 151, 153, 154, 160, 161], "part": [29, 74, 85, 86, 97, 103, 108, 116, 127, 128, 132, 133, 135, 141, 156, 158, 162], "cube": 29, "multipli": [29, 146], "sine": 29, "angl": 29, "arc": 29, "tangent": 29, "arctan": 29, "ourself": [29, 141], "clip": 29, "brake": 29, "preprocess": [29, 36, 47, 60, 65, 66, 73, 74, 77, 85, 86, 87, 88, 89, 90, 91, 93, 95, 98, 99, 100, 107, 108, 110, 116, 119, 130, 131, 132, 134, 135, 137, 139, 140, 144, 147, 149, 150, 151, 153, 154, 155, 164, 176, 178, 180, 184], "linear_model": [29, 44, 47, 73, 77, 80, 82, 85, 86, 87, 89, 91, 95, 99, 100, 107, 108, 110, 127, 130, 131, 132, 134, 135, 136, 137, 139, 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