机器学习的回归模型的一些度量方法

https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics
Regression metrics
See the Regression metrics section of the user guide for further details.

metrics.explained_variance_score(y_true, y_pred) Explained variance regression score function

metrics.mean_absolute_error(y_true, y_pred) Mean absolute error regression loss

metrics.mean_squared_error(y_true, y_pred[, …]) Mean squared error regression loss

metrics.mean_squared_log_error(y_true, y_pred) Mean squared logarithmic error regression loss

metrics.median_absolute_error(y_true, y_pred) Median absolute error regression loss

metrics.r2_score(y_true, y_pred[, …]) R^2 (coefficient of determination) regression score function.

The r2_score function computes R², the coefficient of determination. It provides a measure of how well future samples are likely to be predicted by the model. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.