test

doc/src/week45/week45.do.txt

@@ -141,7 +141,7 @@ plt.show()
 !split
 ===== Using the Voting Classifier =====
 
-We can use the voting classifier on other data sets, here the excting binary case of two distinct objects using the make moons functionality of -Scikit-Learn-.
+We can use the voting classifier on other data sets, here the exciting binary case of two distinct objects using the make moons functionality of _Scikit-Learn_.
 !bc pycod
 from sklearn.model_selection import train_test_split
 from sklearn.datasets import make_moons
@@ -192,7 +192,7 @@ for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
 
 
 !split
-===== Please, not the moons again! Voting and Bagging =====
+===== Voting and Bagging =====
 
 !bc pycod
 from sklearn.model_selection import train_test_split
@@ -295,10 +295,10 @@ The algorithm described here can be applied to both classification and regressio
 
 We will grow of forest of say $B$ trees.
 o For $b=1:B$
-  * Draw a bootstrap sample of from the training data organized in our $\bm{X}$ matrix.
+  * Draw a bootstrap sample from the training data organized in our $\bm{X}$ matrix.
   * We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached
     o we select $m \le p$ variables at random from the $p$ predictors/features
-    o pick the best split point among the $m$ features using either the CART algorithm or the ID3 for classification and create a new node
+    o pick the best split point among the $m$ features using for example the CART algorithm and create a new node
     o split the node into daughter nodes
 o Output then the ensemble of trees $\{T_b\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem.