机器学习的回归模型的一些度量方法

最新推荐文章于 2025-03-04 17:44:34 发布

转载最新推荐文章于 2025-03-04 17:44:34 发布 · 575 阅读

文章标签：

#machine learning

Jack的机器学习专栏同时被 2 个专栏收录

15 篇文章

订阅专栏

机器学习

7 篇文章

订阅专栏

本文详细介绍了Scikit-Learn中用于评估回归模型性能的各种指标，包括解释方差、平均绝对误差、均方误差、均方对数误差、中位数绝对误差和R²分数。R²分数衡量了模型预测未来样本的能力，最佳得分为1.0，可能为负值。

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.
在这里插入图片描述
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
在这里插入图片描述
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.