sklearn 笔记整理：sklearn.mertics

Posted 2021-10-19 UQI-LIUWJ

理论部分：RMSE、MAE等误差指标整理_UQI-LIUWJ的博客-CSDN博客

1 sklearn.metrics.mean_squared_error

sklearn.metrics.mean_squared_error(
    y_true, 
    y_pred, 
    *, 
    sample_weight=None,
    multioutput='uniform_average', 
    squared=True)

1.1 主要参数

y_true	正确值
y_pred	预测值
squared	如果是True，那么就是MSE；否则是RMSE

1.2 使用举例

from sklearn.metrics import mean_squared_error
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_squared_error(y_true, y_pred)
#0.375

2 sklearn.metrics.mean_absolute_error

sklearn.metrics.mean_absolute_error(
    y_true, 
    y_pred, 
    *, 
    sample_weight=None, 
    multioutput='uniform_average')

2.1 主要参数

y_true	正确值
y_pred	预测值

2.2 使用举例 

from sklearn.metrics import mean_absolute_error
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_absolute_error(y_true, y_pred)
#0.5

3 sklearn.metrics.mean_absolute_percentage_error

sklearn.metrics.mean_absolute_percentage_error(
    y_true, 
    y_pred, 
    sample_weight=None, 
    multioutput='uniform_average')

3.1 主要参数

y_true	正确值
y_pred	预测值

3.2 使用举例 

from sklearn.metrics import mean_absolute_percentage_error
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_absolute_percentage_error(y_true, y_pred)
#0.3273809523809524