update

Author: mhjensen

doc/pub/week14/html/week14-bs.html

@@ -1322,8 +1322,8 @@ <h2 id="the-iris-data" class="anchor">The Iris data </h2>
 predictions<span style="color: #666666">=</span>svm_model<span style="color: #666666">.</span>predict(X_test)
 accuracy<span style="color: #666666">=</span>accuracy_score(Y_test,predictions)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the accuracy is: </span><span style="color: #BB6688; font-weight: bold">{</span>accuracy<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
-mocrof1<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
-<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the mocro f1-score is:</span><span style="color: #BB6688; font-weight: bold">{</span>mocrof1<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
+f1score<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
+<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the f1-score is:</span><span style="color: #BB6688; font-weight: bold">{</span>f1score<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
 <span style="color: #008000">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>
@@ -2412,8 +2412,8 @@ <h2 id="iris-dataset" class="anchor">Iris Dataset </h2>
 predictions<span style="color: #666666">=</span>qsvm<span style="color: #666666">.</span>predict(X_test)
 accuracy<span style="color: #666666">=</span>accuracy_score(Y_test,predictions)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the accuracy is: </span><span style="color: #BB6688; font-weight: bold">{</span>accuracy<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121"> %&quot;</span>)
-macrof1<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
-<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the mocro f1-score is: </span><span style="color: #BB6688; font-weight: bold">{</span>mocrof1<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
+f1score<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
+<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the f1-score is: </span><span style="color: #BB6688; font-weight: bold">{</span>f1score<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
 <span style="color: #008000">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>

doc/pub/week14/html/week14-reveal.html

@@ -1225,8 +1225,8 @@ <h2 id="the-iris-data">The Iris data </h2>
 predictions=svm_model.predict(X_test)
 accuracy=accuracy_score(Y_test,predictions)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the accuracy is: {</span>accuracy<span style="color: #CD5555">:.2f}%&quot;</span>)
-mocrof1=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
-<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the mocro f1-score is:{</span>mocrof1<span style="color: #CD5555">:.2f}%&quot;</span>)
+f1score=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the f1-score is:{</span>f1score<span style="color: #CD5555">:.2f}%&quot;</span>)
 <span style="color: #658b00">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>
@@ -2377,8 +2377,8 @@ <h2 id="iris-dataset">Iris Dataset </h2>
 predictions=qsvm.predict(X_test)
 accuracy=accuracy_score(Y_test,predictions)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the accuracy is: {</span>accuracy<span style="color: #CD5555">:.2f} %&quot;</span>)
-macrof1=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
-<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the mocro f1-score is: {</span>mocrof1<span style="color: #CD5555">:.2f}%&quot;</span>)
+f1score=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the f1-score is: {</span>f1score<span style="color: #CD5555">:.2f}%&quot;</span>)
 <span style="color: #658b00">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>

doc/pub/week14/html/week14-solarized.html

@@ -1224,8 +1224,8 @@ <h2 id="the-iris-data">The Iris data </h2>
 predictions=svm_model.predict(X_test)
 accuracy=accuracy_score(Y_test,predictions)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the accuracy is: {</span>accuracy<span style="color: #CD5555">:.2f}%&quot;</span>)
-mocrof1=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
-<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the mocro f1-score is:{</span>mocrof1<span style="color: #CD5555">:.2f}%&quot;</span>)
+f1score=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the f1-score is:{</span>f1score<span style="color: #CD5555">:.2f}%&quot;</span>)
 <span style="color: #658b00">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>
@@ -2304,8 +2304,8 @@ <h2 id="iris-dataset">Iris Dataset </h2>
 predictions=qsvm.predict(X_test)
 accuracy=accuracy_score(Y_test,predictions)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the accuracy is: {</span>accuracy<span style="color: #CD5555">:.2f} %&quot;</span>)
-macrof1=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
-<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the mocro f1-score is: {</span>mocrof1<span style="color: #CD5555">:.2f}%&quot;</span>)
+f1score=f1_score(Y_test,predictions,average=<span style="color: #CD5555">&#39;macro&#39;</span>)
+<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&quot;the f1-score is: {</span>f1score<span style="color: #CD5555">:.2f}%&quot;</span>)
 <span style="color: #658b00">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>

doc/pub/week14/html/week14.html

@@ -1301,8 +1301,8 @@ <h2 id="the-iris-data">The Iris data </h2>
 predictions<span style="color: #666666">=</span>svm_model<span style="color: #666666">.</span>predict(X_test)
 accuracy<span style="color: #666666">=</span>accuracy_score(Y_test,predictions)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the accuracy is: </span><span style="color: #BB6688; font-weight: bold">{</span>accuracy<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
-mocrof1<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
-<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the mocro f1-score is:</span><span style="color: #BB6688; font-weight: bold">{</span>mocrof1<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
+f1score<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
+<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the f1-score is:</span><span style="color: #BB6688; font-weight: bold">{</span>f1score<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
 <span style="color: #008000">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>
@@ -2381,8 +2381,8 @@ <h2 id="iris-dataset">Iris Dataset </h2>
 predictions<span style="color: #666666">=</span>qsvm<span style="color: #666666">.</span>predict(X_test)
 accuracy<span style="color: #666666">=</span>accuracy_score(Y_test,predictions)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the accuracy is: </span><span style="color: #BB6688; font-weight: bold">{</span>accuracy<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121"> %&quot;</span>)
-macrof1<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
-<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the mocro f1-score is: </span><span style="color: #BB6688; font-weight: bold">{</span>mocrof1<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
+f1score<span style="color: #666666">=</span>f1_score(Y_test,predictions,average<span style="color: #666666">=</span><span style="color: #BA2121">&#39;macro&#39;</span>)
+<span style="color: #008000">print</span>(<span style="color: #BA2121">f&quot;the f1-score is: </span><span style="color: #BB6688; font-weight: bold">{</span>f1score<span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&quot;</span>)
 <span style="color: #008000">print</span>(classification_report(Y_test,predictions))
 </pre>
 </div>