均方根误差(Root Mean Square Error, RMSE):即均方误差开根号,方均根偏移代表预测的值和观察到的值之差的样本标准差 from sklearn.metrics import mean_squared_error np.sqrt(mean_squared_error(y_test,y_pre))#y_test为实际值,y_pre为预测值 1. 2. 51.563856309750065 1. 5.平均绝对误差MAE(Mean Absol...
完全匹配的标签甚至有 1 分 >>>fromsklearn.metrics.clusterimportadjusted_rand_score>>>adjusted_rand_score([0,0,1,1], [0,0,1,1])1.0>>>adjusted_rand_score([0,0,1,1], [1,1,0,0])1.0 将所有类成员分配到相同集群的标签是完整的,但可能并不总是纯粹的,因此受到惩罚: >>>adjusted_rand_s...
Adjusted R-Square 抵消样本数量对 R-Square的影响,做到了真正的 0~1,越大越好。 python中可以直接调用 from sklearn.metrics import mean_squared_error #均方误差 from sklearn.metrics import mean_absolute_error #平方绝对误差 from sklearn.metrics import r2_score#R square #调用 MSE:mean_squared_error(...
本文简要介绍python语言中 sklearn.metrics.adjusted_mutual_info_score 的用法。 用法: sklearn.metrics.adjusted_mutual_info_score(labels_true, labels_pred, *, average_method='arithmetic') 两个聚类之间的调整互信息。 调整后的互信息 (AMI) 是对互信息 (MI) 分数的调整,以考虑机会。它解释了这样一个...
Python . We’ve practically seen why adjusted R-squared is a more reliable measure of goodness of fit in multiple regression problems. We’ve discussed the way to interpret R-squared and found out the way to detect overfitting and underfitting using R-squared. ...