from sklearn.metrics import zero_one_loss,log_loss

def test_zero_one_loss():
    y_true=[1,1,1,1,1,0,0,0,0,0]
    y_pred=[0,0,0,1,1,1,1,1,0,0]
    print("zero_one_loss<fraction>:",zero_one_loss(y_true,y_pred,normalize=True))
    print("zero_one_loss<num>:",zero_one_loss(y_true,y_pred,normalize=False))
    
test_zero_one_loss()
 

def test_log_loss():
    y_true=[1, 1, 1, 0, 0, 0]
    y_pred=[[0.1, 0.9],
            [0.2, 0.8],
            [0.3, 0.7],
            [0.7, 0.3],
            [0.8, 0.2],
            [0.9, 0.1]]
    print("log_loss<average>:",log_loss(y_true,y_pred,normalize=True))
    print("log_loss<total>:
 
",log_loss(y_true,y_pred,normalize=False))
    
test_log_loss()

吳裕雄 python 機器學習——模型選擇損失函數模型