I have printed the structure of a CART decision tree, from sci-kit learn, but I don’t understand it.
It’s multiclass classification, there are 4 possible labels, and 5 features. There are 5 different values for each feature. This is what the data looks like
Label Feat1 Feat2 Feat3 Feat4 Feat5
A A B A C A
B B A A B B
C A C C A A
D A B B D D
In order to discretize these categorical variables, I have used a LabelEncoder and OneHotEncoder.
This is the result of printing the structure of the Decision Tree. I know that the gini impurity is the decision tree splitting metric, what I really don’t understand is the top of each box, for example [X7]= 0.5
and the value.