update

doc/src/week44/Programs/DataFiles/grade.dot

@@ -2,12 +2,12 @@ digraph Tree {
 node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
 edge [fontname="helvetica"] ;
 0 [label="X[0] <= 0.5\ngini = 0.48\nsamples = 10\nvalue = [6, 4]", fillcolor="#f6d5bd"] ;
-1 [label="X[5] <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]", fillcolor="#9ccef2"] ;
+1 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
-2 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
-1 -> 2 ;
-3 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4]", fillcolor="#399de5"] ;
-1 -> 3 ;
-4 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
-0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+2 [label="X[2] <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]", fillcolor="#9ccef2"] ;
+0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+3 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
+2 -> 3 ;
+4 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4]", fillcolor="#399de5"] ;
+2 -> 4 ;
 }

doc/src/week44/Programs/grades.py

@@ -45,12 +45,15 @@ display(grades)
 X = grades.loc[:, grades.columns != 'Grade'].values
 y = grades.loc[:, grades.columns == 'Grade'].values
 print(X)
+"""
 # Create the encoder.
 encoder = OneHotEncoder(handle_unknown="ignore")
 # Assume for simplicity all features are categorical.
 encoder.fit(X)    
 # Apply the encoder.
 X = encoder.transform(X)
+print(X)
+"""
 # Then do a Classification tree
 tree_clf = DecisionTreeClassifier(max_depth=2)
 tree_clf.fit(X, y)