问题描述
在 sklearn.model_selection.cross_val_predict 指出：
有人可以解释一下这是什么意思吗？如果这样可以得出每个Y（真实Y）的Y（y预测）估算值，为什么不能使用这些结果来计算诸如RMSE或确定系数的度量？
Can someone explain what does it mean? If this gives estimate of Y (y prediction) for every Y (true Y), why can't I calculate metrics such as RMSE or coefficient of determination using these results?
推荐答案
它似乎基于样本的分组和预测方式。从链接到 cross_val_predict 文档：
It seems to be based on how samples are grouped and predicted. From the user guide linked in the cross_val_predict docs:
cross_val_predict的结果可能与使用
cross_val_score获得的结果不同，因为元素以不同的方式分组。
函数cross_val_score取交叉验证折痕的平均值，即
，而cross_val_predict只是简单地返回来自几个不同模型的标签（或概率）
。因此，cross_val_predict
不能适当地度量泛化误差。
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 似乎说它是所有折叠的平均值，而 cross_val_predict 将单个折叠和不同的模型分组，但不是全部，因此它也不一定会一概而论。例如，使用sklearn页面中的示例代码：
The cross_val_score seems to say that it averages across all of the folds, while the cross_val_predict groups individual folds and distinct models but not all and therefore it won't necessarily generalize as well. For example, using the sample code from the sklearn page:
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
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