1.1 R2求解方式一---从metrics调用r2_socre 1.2 R2求解方式二---从模型调用score 1.3 R2求解方式二---交叉验证调用scoring=r2 2. 校准决定系数Adjusted-R2 3.均方误差MSE(Mean Square Error) 4.均方根误差RMSE(Root Mean ...
1.3 R2求解方式二---交叉验证调用scoring=r2 from sklearn.model_selection import cross_val_scorer2 = cross_val_score(linear,x_test,y_test,cv=10,scoring="r2").mean() # 求的值n次交叉验证后r2的均值r2 0.3803655235719364 2. 校准决定系数Adjusted-R2 校正决定系数是指决定系数R可以用来评价回归方程的...
r2_score(y_true, y_pred) y_true = [[0.5, 1], [-1, 1], [7, -6]] y_pred = [[0, 2], [-1, 2], [8, -5]] r2_score(y_true, y_pred, multioutput='variance_weighted') y_true = [[0.5, 1], [-1, 1], [7, -6]] y_pred = [[0, 2], [-1, 2], [8, -5...
r2_score(y_true, y_pred, multioutput='variance_weighted') y_true = [[0.5,1], [-1,1], [7, -6]] y_pred = [[0,2], [-1,2], [8, -5]] r2_score(y_true, y_pred, multioutput='uniform_average') r2_score(y_true, y_pred, multioutput='raw_values') r2_score(y_true, ...
>>> y_true = [1, 2, 3] >>> y_pred = [1, 2, 3] >>> r2_score(y_true, y_pred) 1.0 >>> y_true = [1, 2, 3] >>> y_pred = [2, 2, 2] >>> r2_score(y_true, y_pred) 0.0 >>> y_true = [1, 2, 3] >>> y_pred = [3, 2, 1] >>> r2_score(y_true...
from sklearn.metrics import r2_score y_true = [3, -0.5, 2, 7]y_pred = [2.5, 0.0, 2, 8]r2_score(y_true, y_pred)y_true = [[0.5, 1], [-1, 1], [7, -6]]y_pred = [[0, 2], [-1, 2], [8, -5]]r2_score(y_true, y_pred, multioutput='variance_weighted'...
ValueError:'wrong_choice'isnota valid scoring value. Valid options are ['accuracy','adjusted_mutual_info_score','adjusted_rand_score','average_precision','completeness_score','explained_variance','f1','f1_macro','f1_micro','f1_samples','f1_weighted','fowlkes_mallows_score','homogeneity_scor...
‘adjusted_rand_score’ metrics.adjusted_rand_score Regression ‘neg_mean_absolute_error’ metrics.mean_absolute_error ‘neg_mean_squared_error’ metrics.mean_squared_error ‘neg_median_absolute_error’ metrics.median_absolute_error ‘r2’ metrics.r2_score 来源...
虽然我不太清楚RidgeCV的原生广义交叉验证是如何工作的,但是如果它确实是离开一出交叉验证的近似值,那么cv_values对象似乎代表的是单个(遗漏的)样本的“r2_score”。但r2_score不适用于单个样本。然后,当评分设置为cv_values时,会在r2_score对象中返回什么?
There is a significant mismatch between r2 score computed by sklearn.model_selection.permutation_test_score and sklearn.metrics.r2_score. The one computed by permutation_test_score seems to be incorrect; See below: import numpy as np from sklearn.linear_model import LinearRegression f...