treatment_cross_val_score

Evaluate a scores by cross-validation.

Parameters

X: numpy ndarray with shape = [n_samples, n_features] Matrix of features. y: numpy array with shape = [n_samples,] Array of target of feature. t: numpy array with shape = [n_samples,] Array of treatments. train_share: float, optional (default=0.7) train_share represents the proportion of the dataset to include in the train split. random_state: int, optional (default=777) random_state is the seed used by the random number generator.

Return

scores: numpy array of floats Array of scores of the estimator for each run of the cross validation.

Examples

from pyuplift.model_selection import treatment_cross_val_score
...
for model_name in models:
    scores = treatment_cross_val_score(X, y, t, models[model_name], cv, seeds=seeds)