RuleTree.evaluation.evaluate_regression

RuleTree.evaluation.evaluate_regression#

evaluate_regression(y_true, y_pred) → dict[source]#

Errors and explained variance of the predictions of a regressor.

Parameters:

  • y_true (array-like of shape (n_samples,)) – True values.

  • y_pred (array-like of shape (n_samples,)) – Predicted values.

Returns:

scores – r2, explained_variance, max_error, mean_absolute_error, mean_squared_error, median_absolute_error, mean_absolute_percentage_error and mean_squared_log_error (nan if some value is negative).

Return type:

dict

See also

sklearn.metrics

the definitions of the metrics.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from RuleTree import RuleTreeRegressor
>>> from RuleTree.evaluation import evaluate_regression
>>> X, y = load_diabetes(return_X_y=True)
>>> model = RuleTreeRegressor(max_depth=3, random_state=0).fit(X, y)
>>> round(evaluate_regression(y, model.predict(X))["r2"], 3)
0.501