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  • sklearn.metrics.mean_absolute_error

    • 注意多维数组 MAE 的计算方法 *
    >>> 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='raw_values')
    array([0.5, 1. ])
    >>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
    ... 
    0.85...
    
    In [34]: y_true = np.array([1,2,3,4,5,0,0,0,0,0])                                                          
    
    In [35]: y_pred = np.array([1.1,2.2,3.1,4.1,5.1,0,0,0,0,0])                                                
    
    In [36]: mean_absolute_error(y_true,y_pred)                                                                
    Out[36]: 0.05999999999999996
    
    In [37]: y_pred = np.array([1.1,2.2,3.1,4.1,5.1])                                                          
    
    In [38]: y_true = np.array([1,2,3,4,5])                                                                    
    
    In [39]: mean_absolute_error(y_true,y_pred)                                                                
    Out[39]: 0.11999999999999993
    
    • multioutput='raw_values' 给出的是每列的 MAE
    • multioutput=[0.3, 0.7] 给出的是加了不同权重的每列的MAE
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  • 原文地址:https://www.cnblogs.com/yaos/p/9878497.html
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