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diff --git a/breastcancer.html b/breastcancer.html
diff --git a/gradient-descent.html b/gradient-descent.html
diff --git a/index.html b/index.html
@@ -25,7 +25,7 @@
 <meta name="author" content="François de Ryckel">
 
 
-<meta name="date" content="2017-11-28">
+<meta name="date" content="2017-12-05">
 
   <meta name="viewport" content="width=device-width, initial-scale=1">
   <meta name="apple-mobile-web-app-capable" content="yes">
@@ -115,14 +115,16 @@
 <li class="chapter" data-level="2.2" data-path="testinference.html"><a href="testinference.html#ttest"><i class="fa fa-check"></i><b>2.2</b> T-tests</a></li>
 <li class="chapter" data-level="2.3" data-path="testinference.html"><a href="testinference.html#anova---analyse-of-variance."><i class="fa fa-check"></i><b>2.3</b> ANOVA - Analyse of variance.</a></li>
 </ul></li>
-<li class="chapter" data-level="3" data-path="mlr.html"><a href="mlr.html"><i class="fa fa-check"></i><b>3</b> Multiple Linear Regression</a><ul>
-<li class="chapter" data-level="3.1" data-path="mlr.html"><a href="mlr.html#single-variable-regression"><i class="fa fa-check"></i><b>3.1</b> Single variable regression</a><ul>
-<li class="chapter" data-level="3.1.1" data-path="mlr.html"><a href="mlr.html#first-example.-predicting-wine-price"><i class="fa fa-check"></i><b>3.1.1</b> First example. Predicting wine price</a></li>
-</ul></li>
+<li class="chapter" data-level="3" data-path="mlr.html"><a href="mlr.html"><i class="fa fa-check"></i><b>3</b> Single &amp; Multiple Linear Regression</a><ul>
+<li class="chapter" data-level="3.1" data-path="mlr.html"><a href="mlr.html#single-variable-regression"><i class="fa fa-check"></i><b>3.1</b> Single variable regression</a></li>
 <li class="chapter" data-level="3.2" data-path="mlr.html"><a href="mlr.html#multi-variables-regression"><i class="fa fa-check"></i><b>3.2</b> Multi-variables regression</a><ul>
-<li class="chapter" data-level="3.2.1" data-path="mlr.html"><a href="mlr.html#first-example.-predicting-wine-price-1"><i class="fa fa-check"></i><b>3.2.1</b> First example. Predicting wine price</a></li>
+<li class="chapter" data-level="3.2.1" data-path="mlr.html"><a href="mlr.html#predicting-wine-price-again"><i class="fa fa-check"></i><b>3.2.1</b> Predicting wine price (again!)</a></li>
+</ul></li>
+<li class="chapter" data-level="3.3" data-path="mlr.html"><a href="mlr.html#model-diagnostic-and-evaluation"><i class="fa fa-check"></i><b>3.3</b> Model diagnostic and evaluation</a></li>
+<li class="chapter" data-level="3.4" data-path="mlr.html"><a href="mlr.html#final-example---boston-dataset---with-backward-elimination"><i class="fa fa-check"></i><b>3.4</b> Final example - Boston dataset - with backward elimination</a><ul>
+<li class="chapter" data-level="3.4.1" data-path="mlr.html"><a href="mlr.html#model-diagmostic"><i class="fa fa-check"></i><b>3.4.1</b> Model diagmostic</a></li>
 </ul></li>
-<li class="chapter" data-level="3.3" data-path="mlr.html"><a href="mlr.html#third-example---boston-dataset"><i class="fa fa-check"></i><b>3.3</b> Third example - Boston dataset</a></li>
+<li class="chapter" data-level="3.5" data-path="mlr.html"><a href="mlr.html#references"><i class="fa fa-check"></i><b>3.5</b> References</a></li>
 </ul></li>
 <li class="chapter" data-level="4" data-path="logistic.html"><a href="logistic.html"><i class="fa fa-check"></i><b>4</b> Logistic Regression</a><ul>
 <li class="chapter" data-level="4.1" data-path="logistic.html"><a href="logistic.html#introduction"><i class="fa fa-check"></i><b>4.1</b> Introduction</a></li>
@@ -135,11 +137,11 @@
 <li class="chapter" data-level="4.6.2" data-path="logistic.html"><a href="logistic.html#imputting-missing-values"><i class="fa fa-check"></i><b>4.6.2</b> Imputting Missing Values</a></li>
 <li class="chapter" data-level="4.6.3" data-path="logistic.html"><a href="logistic.html#roc-and-auc"><i class="fa fa-check"></i><b>4.6.3</b> ROC and AUC</a></li>
 </ul></li>
-<li class="chapter" data-level="4.7" data-path="logistic.html"><a href="logistic.html#references"><i class="fa fa-check"></i><b>4.7</b> References</a></li>
+<li class="chapter" data-level="4.7" data-path="logistic.html"><a href="logistic.html#references-1"><i class="fa fa-check"></i><b>4.7</b> References</a></li>
 </ul></li>
 <li class="chapter" data-level="5" data-path="softmax-and-multinomial-regressions.html"><a href="softmax-and-multinomial-regressions.html"><i class="fa fa-check"></i><b>5</b> Softmax and multinomial regressions</a><ul>
 <li class="chapter" data-level="5.1" data-path="softmax-and-multinomial-regressions.html"><a href="softmax-and-multinomial-regressions.html#multinomial-logistic-regression"><i class="fa fa-check"></i><b>5.1</b> Multinomial Logistic Regression</a></li>
-<li class="chapter" data-level="5.2" data-path="softmax-and-multinomial-regressions.html"><a href="softmax-and-multinomial-regressions.html#references-1"><i class="fa fa-check"></i><b>5.2</b> References</a></li>
+<li class="chapter" data-level="5.2" data-path="softmax-and-multinomial-regressions.html"><a href="softmax-and-multinomial-regressions.html#references-2"><i class="fa fa-check"></i><b>5.2</b> References</a></li>
 </ul></li>
 <li class="chapter" data-level="6" data-path="gradient-descent.html"><a href="gradient-descent.html"><i class="fa fa-check"></i><b>6</b> Gradient Descent</a><ul>
 <li class="chapter" data-level="6.1" data-path="gradient-descent.html"><a href="gradient-descent.html#example-on-functions"><i class="fa fa-check"></i><b>6.1</b> Example on functions</a></li>
@@ -150,7 +152,7 @@
 <li class="chapter" data-level="7.2" data-path="knnchapter.html"><a href="knnchapter.html#example-2.-wine-dataset"><i class="fa fa-check"></i><b>7.2</b> Example 2. Wine dataset</a><ul>
 <li class="chapter" data-level="7.2.1" data-path="knnchapter.html"><a href="knnchapter.html#understand-the-data"><i class="fa fa-check"></i><b>7.2.1</b> Understand the data</a></li>
 </ul></li>
-<li class="chapter" data-level="7.3" data-path="knnchapter.html"><a href="knnchapter.html#references-2"><i class="fa fa-check"></i><b>7.3</b> References</a></li>
+<li class="chapter" data-level="7.3" data-path="knnchapter.html"><a href="knnchapter.html#references-3"><i class="fa fa-check"></i><b>7.3</b> References</a></li>
 </ul></li>
 <li class="chapter" data-level="8" data-path="principal-component-analysis.html"><a href="principal-component-analysis.html"><i class="fa fa-check"></i><b>8</b> Principal Component Analysis</a><ul>
 <li class="chapter" data-level="8.1" data-path="principal-component-analysis.html"><a href="principal-component-analysis.html#pca-on-an-easy-example."><i class="fa fa-check"></i><b>8.1</b> PCA on an easy example.</a></li>
@@ -162,7 +164,7 @@
 <li class="chapter" data-level="9.3" data-path="trees-random-forests-and-classification.html"><a href="trees-random-forests-and-classification.html#second-example."><i class="fa fa-check"></i><b>9.3</b> Second Example.</a></li>
 <li class="chapter" data-level="9.4" data-path="trees-random-forests-and-classification.html"><a href="trees-random-forests-and-classification.html#how-does-a-tree-decide-where-to-split"><i class="fa fa-check"></i><b>9.4</b> How does a tree decide where to split?</a></li>
 <li class="chapter" data-level="9.5" data-path="trees-random-forests-and-classification.html"><a href="trees-random-forests-and-classification.html#third-example."><i class="fa fa-check"></i><b>9.5</b> Third example.</a></li>
-<li class="chapter" data-level="9.6" data-path="trees-random-forests-and-classification.html"><a href="trees-random-forests-and-classification.html#references-3"><i class="fa fa-check"></i><b>9.6</b> References</a></li>
+<li class="chapter" data-level="9.6" data-path="trees-random-forests-and-classification.html"><a href="trees-random-forests-and-classification.html#references-4"><i class="fa fa-check"></i><b>9.6</b> References</a></li>
 </ul></li>
 <li class="chapter" data-level="10" data-path="model-evaluation.html"><a href="model-evaluation.html"><i class="fa fa-check"></i><b>10</b> Model Evaluation</a><ul>
 <li class="chapter" data-level="10.1" data-path="model-evaluation.html"><a href="model-evaluation.html#biais-variance-tradeoff"><i class="fa fa-check"></i><b>10.1</b> Biais variance tradeoff</a></li>
@@ -201,7 +203,7 @@
 <li class="chapter" data-level="13.3.2" data-path="breastcancer.html"><a href="breastcancer.html#pre-process-the-data"><i class="fa fa-check"></i><b>13.3.2</b> Pre-process the data</a></li>
 <li class="chapter" data-level="13.3.3" data-path="breastcancer.html"><a href="breastcancer.html#model-the-data-1"><i class="fa fa-check"></i><b>13.3.3</b> Model the data</a></li>
 </ul></li>
-<li class="chapter" data-level="13.4" data-path="breastcancer.html"><a href="breastcancer.html#references-4"><i class="fa fa-check"></i><b>13.4</b> References</a></li>
+<li class="chapter" data-level="13.4" data-path="breastcancer.html"><a href="breastcancer.html#references-5"><i class="fa fa-check"></i><b>13.4</b> References</a></li>
 </ul></li>
 </ul>
 
@@ -223,7 +225,7 @@ <h1>
 <div id="header">
 <h1 class="title">Machine Learning with R</h1>
 <h4 class="author"><em>François de Ryckel</em></h4>
-<h4 class="date"><em>2017-11-28</em></h4>
+<h4 class="date"><em>2017-12-05</em></h4>
 </div>
 <div id="prerequisites" class="section level1">
 <h1><span class="header-section-number">Chapter 1</span> Prerequisites</h1>
@@ -246,10 +248,19 @@ <h2><span class="header-section-number">1.1</span> Pre-requisite and conventions
 </li>
 <li>Because many of the tidyverse packages do their background work in C++, they are usually pretty efficient in the way they work.</li>
 </ul>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(tidyverse)
-<span class="kw">library</span>(broom)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(broom)
+<span class="kw">library</span>(skimr)
 <span class="kw">library</span>(knitr)
-<span class="kw">library</span>(kableExtra)</code></pre></div>
+<span class="kw">library</span>(kableExtra)
+<span class="kw">library</span>(tidyverse)</code></pre></div>
+<pre><code>## ── Attaching packages ─────────────────────── tidyverse 1.2.1 ──</code></pre>
+<pre><code>## ✔ ggplot2 2.2.1     ✔ purrr   0.2.4
+## ✔ tibble  1.3.4     ✔ dplyr   0.7.4
+## ✔ tidyr   0.7.2     ✔ stringr 1.2.0
+## ✔ readr   1.1.1     ✔ forcats 0.2.0</code></pre>
+<pre><code>## ── Conflicts ────────────────────────── tidyverse_conflicts() ──
+## ✖ dplyr::filter() masks stats::filter()
+## ✖ dplyr::lag()    masks stats::lag()</code></pre>
 <p>Here are some conventions we will be using throughout the book.</p>
 <ul>
 <li><code>df</code> denotes a data frame. Usually the data frame from a raw set of data<br />