corrected typos

Author: mhjensen

doc/LectureNotes/_build/.doctrees/environment.pickle

@@ -32,15 +32,15 @@
     "editable": true
    },
    "source": [
-    "# Overarching aims of the exercises weeks 43 and 44\n",
+    "# Overarching aims of the exercises for week 43\n",
     "\n",
     "The aim of the exercises this week is to gain some confidence with\n",
     "ways to visualize the results of a classification problem.  We will\n",
     "target three ways of setting up the analysis. The first and simplest\n",
     "one is the\n",
-    "1. so-called confusion matrix, and the next is the\n",
+    "1. so-called confusion matrix. The next one is the so-called\n",
     "\n",
-    "2. ROC curve and finally the\n",
+    "2. ROC curve. Finally we have the\n",
     "\n",
     "3. Cumulative gain curve.\n",
     "\n",
@@ -446,7 +446,10 @@
    "id": "be9ff0b9",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -518,7 +521,10 @@
    "id": "d20bb8be",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -547,7 +553,10 @@
    "id": "d271f0ba",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -589,7 +598,10 @@
    "id": "3b045d56",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -611,7 +623,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

doc/LectureNotes/_build/.doctrees/exercisesweek43.doctree

@@ -414,7 +414,7 @@ <h2> Contents </h2>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
 <li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Exercises week 43</a></li>
-<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-weeks-43-and-44">Overarching aims of the exercises weeks 43 and 44</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-for-week-43">Overarching aims of the exercises for week 43</a><ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#confusion-matrix">Confusion Matrix</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#roc-curve">ROC Curve</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cumulative-gain">Cumulative Gain</a></li>
@@ -446,15 +446,15 @@ <h1>Exercises week 43<a class="headerlink" href="#exercises-week-43" title="Link
 <p><strong>October 20-24, 2025</strong></p>
 <p>Date: <strong>Deadline Friday October 24 at midnight</strong></p>
 </section>
-<section class="tex2jax_ignore mathjax_ignore" id="overarching-aims-of-the-exercises-weeks-43-and-44">
-<h1>Overarching aims of the exercises weeks 43 and 44<a class="headerlink" href="#overarching-aims-of-the-exercises-weeks-43-and-44" title="Link to this heading">#</a></h1>
+<section class="tex2jax_ignore mathjax_ignore" id="overarching-aims-of-the-exercises-for-week-43">
+<h1>Overarching aims of the exercises for week 43<a class="headerlink" href="#overarching-aims-of-the-exercises-for-week-43" title="Link to this heading">#</a></h1>
 <p>The aim of the exercises this week is to gain some confidence with
 ways to visualize the results of a classification problem.  We will
 target three ways of setting up the analysis. The first and simplest
 one is the</p>
 <ol class="arabic simple">
-<li><p>so-called confusion matrix, and the next is the</p></li>
-<li><p>ROC curve and finally the</p></li>
+<li><p>so-called confusion matrix. The next one is the so-called</p></li>
+<li><p>ROC curve. Finally we have the</p></li>
 <li><p>Cumulative gain curve.</p></li>
 </ol>
 <p>We will use Logistic Regression as method for the classification in
@@ -615,41 +615,41 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Link to this heading
 Feel free to use these functionalities (we don’t expect you to write your own code for say the confusion matrix).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
-
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.model_selection import  train_test_split 
-# from sklearn.datasets import fill in the data set
-from sklearn.linear_model import LogisticRegression
-
-# Load the data, fill inn
-mydata.data = ?
-
-X_train, X_test, y_train, y_test = train_test_split(mydata.data,cancer.target,random_state=0)
-print(X_train.shape)
-print(X_test.shape)
-# Logistic Regression
-# define which type of problem, binary or multiclass
-logreg = LogisticRegression(solver=&#39;lbfgs&#39;)
-logreg.fit(X_train, y_train)
-
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import cross_validate
-#Cross validation
-accuracy = cross_validate(logreg,X_test,y_test,cv=10)[&#39;test_score&#39;]
-print(accuracy)
-print(&quot;Test set accuracy with Logistic Regression: {:.2f}&quot;.format(logreg.score(X_test,y_test)))
-
-import scikitplot as skplt
-y_pred = logreg.predict(X_test)
-skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
-plt.show()
-y_probas = logreg.predict_proba(X_test)
-skplt.metrics.plot_roc(y_test, y_probas)
-plt.show()
-skplt.metrics.plot_cumulative_gain(y_test, y_probas)
-plt.show()
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span>  <span class="n">train_test_split</span> 
+<span class="c1"># from sklearn.datasets import fill in the data set</span>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+
+<span class="c1"># Load the data, fill inn</span>
+<span class="n">mydata</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="o">?</span>
+
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">mydata</span><span class="o">.</span><span class="n">data</span><span class="p">,</span><span class="n">cancer</span><span class="o">.</span><span class="n">target</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="c1"># Logistic Regression</span>
+<span class="c1"># define which type of problem, binary or multiclass</span>
+<span class="n">logreg</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="s1">&#39;lbfgs&#39;</span><span class="p">)</span>
+<span class="n">logreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_validate</span>
+<span class="c1">#Cross validation</span>
+<span class="n">accuracy</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span><span class="n">logreg</span><span class="p">,</span><span class="n">X_test</span><span class="p">,</span><span class="n">y_test</span><span class="p">,</span><span class="n">cv</span><span class="o">=</span><span class="mi">10</span><span class="p">)[</span><span class="s1">&#39;test_score&#39;</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">accuracy</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test set accuracy with Logistic Regression: </span><span class="si">{:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">logreg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span><span class="n">y_test</span><span class="p">)))</span>
+
+<span class="kn">import</span> <span class="nn">scikitplot</span> <span class="k">as</span> <span class="nn">skplt</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">y_probas</span> <span class="o">=</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_roc</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_probas</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_cumulative_gain</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_probas</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
@@ -664,9 +664,9 @@ <h3>Exercise b)<a class="headerlink" href="#exercise-b" title="Link to this head
 the MNIST data set and just specialize to two numbers. To do so you can use the following code lines</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>from sklearn.datasets import load_digits
-digits = load_digits(n_class=2) # Load only two classes, e.g., 0 and 1
-X, y = digits.data, digits.target
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_digits</span>
+<span class="n">digits</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">(</span><span class="n">n_class</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># Load only two classes, e.g., 0 and 1</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
 </pre></div>
 </div>
 </div>
@@ -678,8 +678,8 @@ <h3>Exercise b)<a class="headerlink" href="#exercise-b" title="Link to this head
 informative features, redundant features, and more.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>from sklearn.datasets import make_classification
-X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=5, n_classes=2, random_state=42)
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -696,10 +696,10 @@ <h3>Exercise c) week 43<a class="headerlink" href="#exercise-c-week-43" title="L
 you can set it up using <strong>scikit-learn</strong>,</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>from sklearn.datasets import load_iris
-iris = load_iris()
-X = iris.data  # Features
-y = iris.target # Target labels
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
+<span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>  <span class="c1"># Features</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span> <span class="c1"># Target labels</span>
 </pre></div>
 </div>
 </div>
@@ -775,7 +775,7 @@ <h3>Exercise c) week 43<a class="headerlink" href="#exercise-c-week-43" title="L
   <nav class="bd-toc-nav page-toc">
     <ul class="visible nav section-nav flex-column">
 <li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Exercises week 43</a></li>
-<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-weeks-43-and-44">Overarching aims of the exercises weeks 43 and 44</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-for-week-43">Overarching aims of the exercises for week 43</a><ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#confusion-matrix">Confusion Matrix</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#roc-curve">ROC Curve</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cumulative-gain">Cumulative Gain</a></li>

doc/LectureNotes/_build/html/_sources/exercisesweek43.ipynb

@@ -32,15 +32,15 @@
     "editable": true
    },
    "source": [
-    "# Overarching aims of the exercises weeks 43 and 44\n",
+    "# Overarching aims of the exercises for week 43\n",
     "\n",
     "The aim of the exercises this week is to gain some confidence with\n",
     "ways to visualize the results of a classification problem.  We will\n",
     "target three ways of setting up the analysis. The first and simplest\n",
     "one is the\n",
-    "1. so-called confusion matrix, and the next is the\n",
+    "1. so-called confusion matrix. The next one is the so-called\n",
     "\n",
-    "2. ROC curve and finally the\n",
+    "2. ROC curve. Finally we have the\n",
     "\n",
     "3. Cumulative gain curve.\n",
     "\n",
@@ -446,7 +446,10 @@
    "id": "be9ff0b9",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -518,7 +521,10 @@
    "id": "d20bb8be",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -547,7 +553,10 @@
    "id": "d271f0ba",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -589,7 +598,10 @@
    "id": "3b045d56",
    "metadata": {
     "collapsed": false,
-    "editable": true
+    "editable": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -611,7 +623,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }