sklearn.metrics.mean_absolute_error用法

平均绝对误差(MAE)

• Mean Absolute Error ，平均绝对误差
• 它表示预测值和观测值之间绝对误差的平均值。
• 是绝对误差的平均值
• 能更好地反映预测值误差的实际情况.
• 用于评估预测结果和真实数据集的接近程度的程度 ，其其值越小说明拟合效果越好

$M A E(X, h)=\frac{1}{m} \sum_{i=1}^{m}\left|h\left(x_{i}\right)-y_{i}\right|$

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

参数

• y_true ：array-like of shape (n_samples,) or (n_samples, n_outputs)

  Ground truth (correct) target values.

• y_pred ： array-like of shape (n_samples,) or (n_samples, n_outputs)

  Estimated target values.

• sample_weight ：array-like of shape (n_samples,), optional 样本权重

• multioutput ：string in [‘raw_values’, ‘uniform_average’] or array-like of shape (n_outputs) 定义多个输出值的聚合。类似数组的值定义用于平均错误的权重。 ‘raw_values’ : Returns a full set of errors in case of multioutput input. ‘uniform_average’ : Errors of all outputs are averaged with uniform weight.

返回值：

• loss：float or ndarray of floats

  If multioutput is ‘raw_values’, then mean absolute error is returned for each output separately. If multioutput is ‘uniform_average’ or an ndarray of weights, then the weighted average of all output errors is returned.

  MAE output is non-negative floating point. The best value is 0.0.

Examples

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

y_true = [[0.5, 1], [-1, 1], [7, -6]]
y_pred = [[0, 2], [-1, 2], [8, -5]]
mean_absolute_error(y_true, y_pred)
# 0.75

mean_absolute_error(y_true, y_pred, multioutput='uniform_average')
# 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])
# 0.85 
# 0.5*0.3+1*0.7=0.85