小编lio*_*063的帖子

我试图从 cross_validation.cross_val_score 生成 R 平方值，约为 0.35，然后将模型应用到同一训练数据集中，并使用“r2_score”函数生成 R 平方值，约为 0.87。我想知道我得到了两个相差如此之大的结果。任何帮助将不胜感激。代码附在下面。

num_folds = 2
num_instances = len(X_train)
scoring ='r2'

models = []
models.append(('RF', RandomForestRegressor()))
results = []
names = []
for name, model in models:
    kfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, random_state=seed)
    cv_results = cross_validation.cross_val_score(model, X_train, Y_train, cv=kfold,
    scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

model.fit(X_train, Y_train)
train_pred=model.predict(X_train)
r2_score(Y_train, train_pred)