回归类(Regression)问题中 比较常用的是 'neg_mean_squared_error‘ 也就是 均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值 公式长这样,了解下就ok了 K折交叉验证:sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None) n_splits:表示划分几等份 shuffle:在...
真正的均方误差MSE的数值,其实就是neg_mean_squared_error去掉负号的数字。
第四个是划分份数n,这里cv=10是将数据集分了10份。 第五个是返回的衡量指标,默认是R平方,这里的是neg_mean_squared_error是负均方误差 注意,R平方越接近1说明效果越好,很有意思的是R平方也可以为负数,但是一旦为负数时,说明曲线拟合效果不是很好,最好更换估计量。 负均方误差我们一般将其乘以负一得到均方误差...
# 定义特征矩阵 X 和目标变量 y # 使用交叉验证计算均方误差 mse_scores = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=5) # 将均方误差转换为正值 mse_scores = -mse_scores # 计算均值和标准差 mean_mse = mse_scores.mean() std_mse = mse_scores.std() 参数...
assert_array_almost_equal(r2_scores, [0.94,0.97,0.97,0.99,0.92],2)# Mean squared error; this is a loss function, so "scores" are negativeneg_mse_scores = cval.cross_val_score(reg, X, y, cv=5, scoring="neg_mean_squared_error") ...
'neg_mean_absolute_error’ metrics.mean_absolute_error 'neg_mean_squared_error’ metrics.mean_squared_error 'neg_root_mean_squared_error’ metrics.mean_squared_error 'neg_mean_squared_log_error’ metrics.mean_squared_log_error 'neg_median_absolute_error’ ...
(metrics.mean_squared_error(targets, rf_preds, squared=False)))scores = cross_val_score(rf, train_, targets, cv=7, scoring='neg_root_mean_squared_error', n_jobs=7)print("RMSE Score using cv_score: {:0.5f}".format(scores.mean() * -1))RMSE Score using cv preds: 0.01658RMSE ...
模型的度量指标和损失函数有什么区别？为什么在项目中两者都很重要？
format(mean_squared_error(y, rf_preds, squared=False))) scores = cross_val_score(rf, X, y, cv=kf, scoring='neg_root_mean_squared_error', n_jobs=5) print("RMSE Score using cv_score:\t", "{:0.5f}".format(scores.mean() * -1)) print("RMSE Score using cv...
The Mean Square Error returned by sklearn.cross_validation.cross_val_score is always a negative. While being a designed decision so that the output of this function can be used for maximization given some hyperparameters, it's extremely ...