about sci-learn packet of Decision Tree(CART)

[mimiao2017]

classifier:

criterion : string, optional (default=”gini”). Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.

regressor:

criterion : Supported criteria are “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node, “friedman_mse”, which uses mean squared error with Friedman’s improvement score for potential splits, and “mae” for the mean absolute error, which minimizes the L1 loss using the median of each terminal node.

impurity decrease = N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity)