机器学习常用误差函数_sklearn.metrics

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我是靠谱客的博主健壮煎饼，最近开发中收集的这篇文章主要介绍机器学习常用误差函数_sklearn.metrics，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

1. Max error_最大误差，数学表达式

$Max Error(y, hat{y}) = max(|y_{i} - hat{y_{i}}|)$

from sklearn.metrics import max_error
y_true = [3, 2, 7, 1]
y_pred = [9, 2, 7, 1]
max_error(y_true, y_pred)

output : 6

2. Mean absolute error_平均绝对误差，数学表达式

$MAE(y, hat{y}) = frac{1}{n_{samples}}sum_{i=0}^{n_{samples}-1}|y_i - hat{y_i}|$

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)
output : 0.5
y_true = [[0.5, 1], [-1, 1], [7, -6]]
y_pred = [[0, 2], [-1, 2], [8, -5]]
mean_absolute_error(y_true, y_pred)
output : 0.75...
mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([0.5, 1. ])
mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
output : 0.85

3.Mean squared error_均方误差，数学表达式：

$MSE(y, hat{y}) = frac{1}{n_{samples}}sum_{i=0}^{n_{samples}-1}(y_i - hat{y_i})^{2}$

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)
output : 0.375
y_true = [[0.5, 1], [-1, 1], [7, -6]]
y_pred = [[0, 2], [-1, 2], [8, -5]]
mean_squared_error(y_true, y_pred)
output : 0.7083...

4. Mean squared logarithmic error_均方对数误差，数学表达式：

$MSLE(y, hat{y}) = frac{1}{n_{samples}}sum_{i=0}^{n_{samples}-1}(log_e(1+y_i) - log_e(1+hat{y_i}))^{2}$

from sklearn.metrics import mean_squared_log_error
y_true = [3, 5, 2.5, 7]
y_pred = [2.5, 5, 4, 8]
mean_squared_log_error(y_true, y_pred)
output : 0.039...
y_true = [[0.5, 1], [1, 2], [7, 6]]
y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
mean_squared_log_error(y_true, y_pred)
output : 0.044...

5.Median absolute error_中值绝对误差，数学表达式

$MedAE(y, hat{y})=median(|y_1-hat{y_1|}, ..., |y_n - hat{y_n}|)$

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