[ci skip] ENH Improve wording in group-aware cross-validation notebook (#776)

Co-authored-by: ArturoAmorQ <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]> 6c73552

Author: glemaitre

_images/040319a5f0195151ade57c2b00d5a026012efc5f64c4fae5dfd2c1ed08598537.png

@@ -7,9 +7,8 @@
 
 # %% [markdown]
 # # Sample grouping
-# We are going to linger into the concept of sample groups. As in the previous
-# section, we will give an example to highlight some surprising results. This
-# time, we will use the handwritten digits dataset.
+# In this notebook we present the concept of **sample groups**. We use the
+# handwritten digits dataset to highlight some surprising results.
 
 # %%
 from sklearn.datasets import load_digits
@@ -18,8 +17,17 @@
 data, target = digits.data, digits.target
 
 # %% [markdown]
-# We will recreate the same model used in the previous notebook: a logistic
-# regression classifier with a preprocessor to scale the data.
+# We create a model consisting of a logistic regression classifier with a
+# preprocessor to scale the data.
+#
+# ```{note}
+# Here we use a `MinMaxScaler` as we know that each pixel's gray-scale is
+# strictly bounded between 0 (white) and 16 (black). This makes `MinMaxScaler`
+# more suited in this case than `StandardScaler`, as some pixels consistently
+# have low variance (pixels at the borders might almost always be zero if most
+# digits are centered in the image). Then, using `StandardScaler` can result in
+# a very high scaled value due to division by a small number.
+# ```
 
 # %%
 from sklearn.preprocessing import MinMaxScaler
@@ -29,8 +37,10 @@
 model = make_pipeline(MinMaxScaler(), LogisticRegression(max_iter=1_000))
 
 # %% [markdown]
-# We will use the same baseline model. We will use a `KFold` cross-validation
-# without shuffling the data at first.
+# The idea is to compare the estimated generalization performance using
+# different cross-validation techniques and see how such estimations are
+# impacted by underlying data structures. We first use a `KFold`
+# cross-validation without shuffling the data.
 
 # %%
 from sklearn.model_selection import cross_val_score, KFold
@@ -59,9 +69,9 @@
 )
 
 # %% [markdown]
-# We observe that shuffling the data improves the mean accuracy. We could go a
-# little further and plot the distribution of the testing score. We can first
-# concatenate the test scores.
+# We observe that shuffling the data improves the mean accuracy. We can go a
+# little further and plot the distribution of the testing score. For such
+# purpose we concatenate the test scores.
 
 # %%
 import pandas as pd
@@ -72,29 +82,29 @@
 ).T
 
 # %% [markdown]
-# Let's plot the distribution now.
+# Let's now plot the score distributions.
 
 # %%
 import matplotlib.pyplot as plt
 
-all_scores.plot.hist(bins=10, edgecolor="black", alpha=0.7)
+all_scores.plot.hist(bins=16, edgecolor="black", alpha=0.7)
 plt.xlim([0.8, 1.0])
 plt.xlabel("Accuracy score")
 plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
 _ = plt.title("Distribution of the test scores")
 
 # %% [markdown]
-# The cross-validation testing error that uses the shuffling has less variance
-# than the one that does not impose any shuffling. It means that some specific
-# fold leads to a low score in this case.
+# Shuffling the data results in a higher cross-validated test accuracy with less
+# variance compared to when the data is not shuffled. It means that some
+# specific fold leads to a low score in this case.
 
 # %%
 print(test_score_no_shuffling)
 
 # %% [markdown]
-# Thus, there is an underlying structure in the data that shuffling will break
-# and get better results. To get a better understanding, we should read the
-# documentation shipped with the dataset.
+# Thus, shuffling the data breaks the underlying structure and thus makes the
+# classification task easier to our model. To get a better understanding, we can
+# read the dataset description in more detail:
 
 # %%
 print(digits.DESCR)
@@ -165,7 +175,7 @@
     groups[lb:up] = group_id
 
 # %% [markdown]
-# We can check the grouping by plotting the indices linked to writer ids.
+# We can check the grouping by plotting the indices linked to writers' ids.
 
 # %%
 plt.plot(groups)
@@ -176,8 +186,9 @@
 _ = plt.title("Underlying writer groups existing in the target")
 
 # %% [markdown]
-# Once we group the digits by writer, we can use cross-validation to take this
-# information into account: the class containing `Group` should be used.
+# Once we group the digits by writer, we can incorporate this information into
+# the cross-validation process by using group-aware variations of the strategies
+# we have explored in this course, for example, the `GroupKFold` strategy.
 
 # %%
 from sklearn.model_selection import GroupKFold
@@ -191,10 +202,12 @@
 )
 
 # %% [markdown]
-# We see that this strategy is less optimistic regarding the model
-# generalization performance. However, this is the most reliable if our goal is
-# to make handwritten digits recognition writers independent. Besides, we can as
-# well see that the standard deviation was reduced.
+# We see that this strategy leads to a lower generalization performance than the
+# other two techniques. However, this is the most reliable estimate if our goal
+# is to evaluate the capabilities of the model to generalize to new unseen
+# writers. In this sense, shuffling the dataset (or alternatively using the
+# writers' ids as a new feature) would lead the model to memorize the different
+# writer's particular handwriting.
 
 # %%
 all_scores = pd.DataFrame(
@@ -207,13 +220,18 @@
 ).T
 
 # %%
-all_scores.plot.hist(bins=10, edgecolor="black", alpha=0.7)
+all_scores.plot.hist(bins=16, edgecolor="black", alpha=0.7)
 plt.xlim([0.8, 1.0])
 plt.xlabel("Accuracy score")
 plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
 _ = plt.title("Distribution of the test scores")
 
 # %% [markdown]
-# As a conclusion, it is really important to take any sample grouping pattern
-# into account when evaluating a model. Otherwise, the results obtained will be
-# over-optimistic in regards with reality.
+# In conclusion, accounting for any sample grouping patterns is crucial when
+# assessing a model’s ability to generalize to new groups. Without this
+# consideration, the results may appear overly optimistic compared to the actual
+# performance.
+#
+# The interested reader can learn about other group-aware cross-validation
+# techniques in the [scikit-learn user
+# guide](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data).