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correction to code
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Lines changed: 186 additions & 298 deletions

‎doc/pub/DecisionTrees/html/._DecisionTrees-bs050.html‎

Lines changed: 13 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -255,29 +255,19 @@ <h2 id="___sec49" class="anchor">AdaBoost Examples </h2>
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algorithm<span style="color: #666666">=</span><span style="color: #BA2121">&quot;SAMME.R&quot;</span>, learning_rate<span style="color: #666666">=0.5</span>, random_state<span style="color: #666666">=42</span>)
256256
ada_clf<span style="color: #666666">.</span>fit(X_train, y_train)
257257

258-
plot_decision_boundary(ada_clf, X, y)
259-
260-
m <span style="color: #666666">=</span> <span style="color: #008000">len</span>(X_train)
261-
262-
plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">11</span>, <span style="color: #666666">4</span>))
263-
<span style="color: #008000; font-weight: bold">for</span> subplot, learning_rate <span style="color: #AA22FF; font-weight: bold">in</span> ((<span style="color: #666666">121</span>, <span style="color: #666666">1</span>), (<span style="color: #666666">122</span>, <span style="color: #666666">0.5</span>)):
264-
sample_weights <span style="color: #666666">=</span> np<span style="color: #666666">.</span>ones(m)
265-
plt<span style="color: #666666">.</span>subplot(subplot)
266-
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">5</span>):
267-
svm_clf <span style="color: #666666">=</span> SVC(kernel<span style="color: #666666">=</span><span style="color: #BA2121">&quot;rbf&quot;</span>, C<span style="color: #666666">=0.05</span>, gamma<span style="color: #666666">=</span><span style="color: #BA2121">&quot;auto&quot;</span>, random_state<span style="color: #666666">=42</span>)
268-
svm_clf<span style="color: #666666">.</span>fit(X_train, y_train, sample_weight<span style="color: #666666">=</span>sample_weights)
269-
y_pred <span style="color: #666666">=</span> svm_clf<span style="color: #666666">.</span>predict(X_train)
270-
sample_weights[y_pred <span style="color: #666666">!=</span> y_train] <span style="color: #666666">*=</span> (<span style="color: #666666">1</span> <span style="color: #666666">+</span> learning_rate)
271-
plot_decision_boundary(svm_clf, X, y, alpha<span style="color: #666666">=0.2</span>)
272-
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;learning_rate = {}&quot;</span><span style="color: #666666">.</span>format(learning_rate), fontsize<span style="color: #666666">=16</span>)
273-
<span style="color: #008000; font-weight: bold">if</span> subplot <span style="color: #666666">==</span> <span style="color: #666666">121</span>:
274-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.7</span>, <span style="color: #666666">-0.65</span>, <span style="color: #BA2121">&quot;1&quot;</span>, fontsize<span style="color: #666666">=14</span>)
275-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.6</span>, <span style="color: #666666">-0.10</span>, <span style="color: #BA2121">&quot;2&quot;</span>, fontsize<span style="color: #666666">=14</span>)
276-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.5</span>, <span style="color: #666666">0.10</span>, <span style="color: #BA2121">&quot;3&quot;</span>, fontsize<span style="color: #666666">=14</span>)
277-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.4</span>, <span style="color: #666666">0.55</span>, <span style="color: #BA2121">&quot;4&quot;</span>, fontsize<span style="color: #666666">=14</span>)
278-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.3</span>, <span style="color: #666666">0.90</span>, <span style="color: #BA2121">&quot;5&quot;</span>, fontsize<span style="color: #666666">=14</span>)
279-
280-
save_fig(<span style="color: #BA2121">&quot;boosting_plot&quot;</span>)
258+
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> AdaBoostClassifier
259+
260+
ada_clf <span style="color: #666666">=</span> AdaBoostClassifier(
261+
DecisionTreeClassifier(max_depth<span style="color: #666666">=1</span>), n_estimators<span style="color: #666666">=200</span>,
262+
algorithm<span style="color: #666666">=</span><span style="color: #BA2121">&quot;SAMME.R&quot;</span>, learning_rate<span style="color: #666666">=0.5</span>, random_state<span style="color: #666666">=42</span>)
263+
ada_clf<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
264+
y_pred <span style="color: #666666">=</span> ada_clf<span style="color: #666666">.</span>predict(X_test_scaled)
265+
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000">True</span>)
266+
plt<span style="color: #666666">.</span>show()
267+
y_probas <span style="color: #666666">=</span> ada_clf<span style="color: #666666">.</span>predict_proba(X_test_scaled)
268+
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_roc(y_test, y_probas)
269+
plt<span style="color: #666666">.</span>show()
270+
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_cumulative_gain(y_test, y_probas)
281271
plt<span style="color: #666666">.</span>show()
282272
</pre></div>
283273
<p>

‎doc/pub/DecisionTrees/html/DecisionTrees-reveal.html‎

Lines changed: 12 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -2169,29 +2169,19 @@ <h2 id="___sec49">AdaBoost Examples </h2>
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algorithm=<span style="color: #CD5555">&quot;SAMME.R&quot;</span>, learning_rate=<span style="color: #B452CD">0.5</span>, random_state=<span style="color: #B452CD">42</span>)
21702170
ada_clf.fit(X_train, y_train)
21712171

2172-
plot_decision_boundary(ada_clf, X, y)
2172+
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> AdaBoostClassifier
21732173

2174-
m = <span style="color: #658b00">len</span>(X_train)
2175-
2176-
plt.figure(figsize=(<span style="color: #B452CD">11</span>, <span style="color: #B452CD">4</span>))
2177-
<span style="color: #8B008B; font-weight: bold">for</span> subplot, learning_rate <span style="color: #8B008B">in</span> ((<span style="color: #B452CD">121</span>, <span style="color: #B452CD">1</span>), (<span style="color: #B452CD">122</span>, <span style="color: #B452CD">0.5</span>)):
2178-
sample_weights = np.ones(m)
2179-
plt.subplot(subplot)
2180-
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #B452CD">5</span>):
2181-
svm_clf = SVC(kernel=<span style="color: #CD5555">&quot;rbf&quot;</span>, C=<span style="color: #B452CD">0.05</span>, gamma=<span style="color: #CD5555">&quot;auto&quot;</span>, random_state=<span style="color: #B452CD">42</span>)
2182-
svm_clf.fit(X_train, y_train, sample_weight=sample_weights)
2183-
y_pred = svm_clf.predict(X_train)
2184-
sample_weights[y_pred != y_train] *= (<span style="color: #B452CD">1</span> + learning_rate)
2185-
plot_decision_boundary(svm_clf, X, y, alpha=<span style="color: #B452CD">0.2</span>)
2186-
plt.title(<span style="color: #CD5555">&quot;learning_rate = {}&quot;</span>.format(learning_rate), fontsize=<span style="color: #B452CD">16</span>)
2187-
<span style="color: #8B008B; font-weight: bold">if</span> subplot == <span style="color: #B452CD">121</span>:
2188-
plt.text(-<span style="color: #B452CD">0.7</span>, -<span style="color: #B452CD">0.65</span>, <span style="color: #CD5555">&quot;1&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2189-
plt.text(-<span style="color: #B452CD">0.6</span>, -<span style="color: #B452CD">0.10</span>, <span style="color: #CD5555">&quot;2&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2190-
plt.text(-<span style="color: #B452CD">0.5</span>, <span style="color: #B452CD">0.10</span>, <span style="color: #CD5555">&quot;3&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2191-
plt.text(-<span style="color: #B452CD">0.4</span>, <span style="color: #B452CD">0.55</span>, <span style="color: #CD5555">&quot;4&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2192-
plt.text(-<span style="color: #B452CD">0.3</span>, <span style="color: #B452CD">0.90</span>, <span style="color: #CD5555">&quot;5&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2193-
2194-
save_fig(<span style="color: #CD5555">&quot;boosting_plot&quot;</span>)
2174+
ada_clf = AdaBoostClassifier(
2175+
DecisionTreeClassifier(max_depth=<span style="color: #B452CD">1</span>), n_estimators=<span style="color: #B452CD">200</span>,
2176+
algorithm=<span style="color: #CD5555">&quot;SAMME.R&quot;</span>, learning_rate=<span style="color: #B452CD">0.5</span>, random_state=<span style="color: #B452CD">42</span>)
2177+
ada_clf.fit(X_train_scaled, y_train)
2178+
y_pred = ada_clf.predict(X_test_scaled)
2179+
skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=<span style="color: #658b00">True</span>)
2180+
plt.show()
2181+
y_probas = ada_clf.predict_proba(X_test_scaled)
2182+
skplt.metrics.plot_roc(y_test, y_probas)
2183+
plt.show()
2184+
skplt.metrics.plot_cumulative_gain(y_test, y_probas)
21952185
plt.show()
21962186
</pre></div>
21972187
</section>

‎doc/pub/DecisionTrees/html/DecisionTrees-solarized.html‎

Lines changed: 12 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -2153,29 +2153,19 @@ <h2 id="___sec49">AdaBoost Examples </h2>
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algorithm=<span style="color: #CD5555">&quot;SAMME.R&quot;</span>, learning_rate=<span style="color: #B452CD">0.5</span>, random_state=<span style="color: #B452CD">42</span>)
21542154
ada_clf.fit(X_train, y_train)
21552155

2156-
plot_decision_boundary(ada_clf, X, y)
2156+
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> AdaBoostClassifier
21572157

2158-
m = <span style="color: #658b00">len</span>(X_train)
2159-
2160-
plt.figure(figsize=(<span style="color: #B452CD">11</span>, <span style="color: #B452CD">4</span>))
2161-
<span style="color: #8B008B; font-weight: bold">for</span> subplot, learning_rate <span style="color: #8B008B">in</span> ((<span style="color: #B452CD">121</span>, <span style="color: #B452CD">1</span>), (<span style="color: #B452CD">122</span>, <span style="color: #B452CD">0.5</span>)):
2162-
sample_weights = np.ones(m)
2163-
plt.subplot(subplot)
2164-
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #B452CD">5</span>):
2165-
svm_clf = SVC(kernel=<span style="color: #CD5555">&quot;rbf&quot;</span>, C=<span style="color: #B452CD">0.05</span>, gamma=<span style="color: #CD5555">&quot;auto&quot;</span>, random_state=<span style="color: #B452CD">42</span>)
2166-
svm_clf.fit(X_train, y_train, sample_weight=sample_weights)
2167-
y_pred = svm_clf.predict(X_train)
2168-
sample_weights[y_pred != y_train] *= (<span style="color: #B452CD">1</span> + learning_rate)
2169-
plot_decision_boundary(svm_clf, X, y, alpha=<span style="color: #B452CD">0.2</span>)
2170-
plt.title(<span style="color: #CD5555">&quot;learning_rate = {}&quot;</span>.format(learning_rate), fontsize=<span style="color: #B452CD">16</span>)
2171-
<span style="color: #8B008B; font-weight: bold">if</span> subplot == <span style="color: #B452CD">121</span>:
2172-
plt.text(-<span style="color: #B452CD">0.7</span>, -<span style="color: #B452CD">0.65</span>, <span style="color: #CD5555">&quot;1&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2173-
plt.text(-<span style="color: #B452CD">0.6</span>, -<span style="color: #B452CD">0.10</span>, <span style="color: #CD5555">&quot;2&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2174-
plt.text(-<span style="color: #B452CD">0.5</span>, <span style="color: #B452CD">0.10</span>, <span style="color: #CD5555">&quot;3&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2175-
plt.text(-<span style="color: #B452CD">0.4</span>, <span style="color: #B452CD">0.55</span>, <span style="color: #CD5555">&quot;4&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2176-
plt.text(-<span style="color: #B452CD">0.3</span>, <span style="color: #B452CD">0.90</span>, <span style="color: #CD5555">&quot;5&quot;</span>, fontsize=<span style="color: #B452CD">14</span>)
2177-
2178-
save_fig(<span style="color: #CD5555">&quot;boosting_plot&quot;</span>)
2158+
ada_clf = AdaBoostClassifier(
2159+
DecisionTreeClassifier(max_depth=<span style="color: #B452CD">1</span>), n_estimators=<span style="color: #B452CD">200</span>,
2160+
algorithm=<span style="color: #CD5555">&quot;SAMME.R&quot;</span>, learning_rate=<span style="color: #B452CD">0.5</span>, random_state=<span style="color: #B452CD">42</span>)
2161+
ada_clf.fit(X_train_scaled, y_train)
2162+
y_pred = ada_clf.predict(X_test_scaled)
2163+
skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=<span style="color: #658b00">True</span>)
2164+
plt.show()
2165+
y_probas = ada_clf.predict_proba(X_test_scaled)
2166+
skplt.metrics.plot_roc(y_test, y_probas)
2167+
plt.show()
2168+
skplt.metrics.plot_cumulative_gain(y_test, y_probas)
21792169
plt.show()
21802170
</pre></div>
21812171
<p>

‎doc/pub/DecisionTrees/html/DecisionTrees.html‎

Lines changed: 12 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -2158,29 +2158,19 @@ <h2 id="___sec49">AdaBoost Examples </h2>
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algorithm<span style="color: #666666">=</span><span style="color: #BA2121">&quot;SAMME.R&quot;</span>, learning_rate<span style="color: #666666">=0.5</span>, random_state<span style="color: #666666">=42</span>)
21592159
ada_clf<span style="color: #666666">.</span>fit(X_train, y_train)
21602160

2161-
plot_decision_boundary(ada_clf, X, y)
2161+
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> AdaBoostClassifier
21622162

2163-
m <span style="color: #666666">=</span> <span style="color: #008000">len</span>(X_train)
2164-
2165-
plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">11</span>, <span style="color: #666666">4</span>))
2166-
<span style="color: #008000; font-weight: bold">for</span> subplot, learning_rate <span style="color: #AA22FF; font-weight: bold">in</span> ((<span style="color: #666666">121</span>, <span style="color: #666666">1</span>), (<span style="color: #666666">122</span>, <span style="color: #666666">0.5</span>)):
2167-
sample_weights <span style="color: #666666">=</span> np<span style="color: #666666">.</span>ones(m)
2168-
plt<span style="color: #666666">.</span>subplot(subplot)
2169-
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">5</span>):
2170-
svm_clf <span style="color: #666666">=</span> SVC(kernel<span style="color: #666666">=</span><span style="color: #BA2121">&quot;rbf&quot;</span>, C<span style="color: #666666">=0.05</span>, gamma<span style="color: #666666">=</span><span style="color: #BA2121">&quot;auto&quot;</span>, random_state<span style="color: #666666">=42</span>)
2171-
svm_clf<span style="color: #666666">.</span>fit(X_train, y_train, sample_weight<span style="color: #666666">=</span>sample_weights)
2172-
y_pred <span style="color: #666666">=</span> svm_clf<span style="color: #666666">.</span>predict(X_train)
2173-
sample_weights[y_pred <span style="color: #666666">!=</span> y_train] <span style="color: #666666">*=</span> (<span style="color: #666666">1</span> <span style="color: #666666">+</span> learning_rate)
2174-
plot_decision_boundary(svm_clf, X, y, alpha<span style="color: #666666">=0.2</span>)
2175-
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;learning_rate = {}&quot;</span><span style="color: #666666">.</span>format(learning_rate), fontsize<span style="color: #666666">=16</span>)
2176-
<span style="color: #008000; font-weight: bold">if</span> subplot <span style="color: #666666">==</span> <span style="color: #666666">121</span>:
2177-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.7</span>, <span style="color: #666666">-0.65</span>, <span style="color: #BA2121">&quot;1&quot;</span>, fontsize<span style="color: #666666">=14</span>)
2178-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.6</span>, <span style="color: #666666">-0.10</span>, <span style="color: #BA2121">&quot;2&quot;</span>, fontsize<span style="color: #666666">=14</span>)
2179-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.5</span>, <span style="color: #666666">0.10</span>, <span style="color: #BA2121">&quot;3&quot;</span>, fontsize<span style="color: #666666">=14</span>)
2180-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.4</span>, <span style="color: #666666">0.55</span>, <span style="color: #BA2121">&quot;4&quot;</span>, fontsize<span style="color: #666666">=14</span>)
2181-
plt<span style="color: #666666">.</span>text(<span style="color: #666666">-0.3</span>, <span style="color: #666666">0.90</span>, <span style="color: #BA2121">&quot;5&quot;</span>, fontsize<span style="color: #666666">=14</span>)
2182-
2183-
save_fig(<span style="color: #BA2121">&quot;boosting_plot&quot;</span>)
2163+
ada_clf <span style="color: #666666">=</span> AdaBoostClassifier(
2164+
DecisionTreeClassifier(max_depth<span style="color: #666666">=1</span>), n_estimators<span style="color: #666666">=200</span>,
2165+
algorithm<span style="color: #666666">=</span><span style="color: #BA2121">&quot;SAMME.R&quot;</span>, learning_rate<span style="color: #666666">=0.5</span>, random_state<span style="color: #666666">=42</span>)
2166+
ada_clf<span style="color: #666666">.</span>fit(X_train_scaled, y_train)
2167+
y_pred <span style="color: #666666">=</span> ada_clf<span style="color: #666666">.</span>predict(X_test_scaled)
2168+
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_confusion_matrix(y_test, y_pred, normalize<span style="color: #666666">=</span><span style="color: #008000">True</span>)
2169+
plt<span style="color: #666666">.</span>show()
2170+
y_probas <span style="color: #666666">=</span> ada_clf<span style="color: #666666">.</span>predict_proba(X_test_scaled)
2171+
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_roc(y_test, y_probas)
2172+
plt<span style="color: #666666">.</span>show()
2173+
skplt<span style="color: #666666">.</span>metrics<span style="color: #666666">.</span>plot_cumulative_gain(y_test, y_probas)
21842174
plt<span style="color: #666666">.</span>show()
21852175
</pre></div>
21862176
<p>

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