Implementing a decision tree from scratch

We develop the CART tree algorithm by hand on a toy dataset as follows:

Figure 4.8: An example of ad data

To begin with, we decide on the first splitting point, the root, by trying out all possible values for each of the two features. We utilize the weighted_impurity function we just defined to calculate the weighted Gini Impurity for each possible combination, as follows:

Gini(interest, tech) = weighted_impurity([[1, 1, 0],
    [0, 0, 0, 1]]) = 0.405

Here, if we partition according to whether the user interest is tech, we have the 1^st, 5^th, and 6^th samples for one group and the remaining samples for another group. Then the classes for the first group are [1, 1, 0], and the classes for the second group are [0, 0, 0, 1]:

Gini(interest, Fashion) = weighted_impurity([[0, 0],
    [1, 0, 1, 0, 1]]) = 0.343

Here, if we partition according to whether the user's interest is fashion, we have the 2^nd and...