在sklearn.model_selection.cross_val_predict page据称:
Generate cross-validated estimates for each input data point. It is not appropriate to pass these predictions into an evaluation metric.
谁能解释一下这是什么意思吗？如果这给出了每个 Y(真实 Y)的 Y 估计(y 预测)，为什么我不能使用这些结果计算 RMSE 或确定系数等指标？
最佳答案
它似乎基于样本的分组和预测方式。来自 user guide链接在 cross_val_predict 文档中:
Warning Note on inappropriate usage of cross_val_predict
The result of cross_val_predict may be different from those obtained using cross_val_score as the elements are grouped in different ways. The function cross_val_score takes an average over cross-validation folds, whereas cross_val_predict simply returns the labels (or probabilities) from several distinct models undistinguished. Thus, cross_val_predict is not an appropriate measure of generalisation error.
cross_val_score 似乎表示它对所有 折叠进行平均，而 cross_val_predict 对各个折叠和不同模型进行分组，但不是全部因此它也不一定具有普遍性。例如，使用 sklearn 页面中的示例代码:
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_predict, cross_val_score
from sklearn.metrics import mean_squared_error, make_scorer
diabetes = datasets.load_diabetes()
X = diabetes.data[:200]
y = diabetes.target[:200]
lasso = linear_model.Lasso()
y_pred = cross_val_predict(lasso, X, y, cv=3)
print("Cross Val Prediction score:{}".format(mean_squared_error(y,y_pred)))
print("Cross Val Score:{}".format(np.mean(cross_val_score(lasso, X, y, cv=3, scoring = make_scorer(mean_squared_error)))))
Cross Val Prediction score:3993.771257795029
Cross Val Score:3997.1789145156217
关于python - 使用 cross_val_predict sklearn 计算评估指标，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/53523887/