KFlod 适用于用户回归类型数据划分
stratifiedKFlod 适用于分类数据划分
并且在实验中也发现，stratifiedKFlod.split(X_train,y_train)的y_train不可为连续数据，因此无法使用，只能用KFold
models = [GBDT(n_estimators=100),
RF(n_estimators=100),
ET(n_estimators=100),
ADA(n_estimators=100)]
X_train_stack = np.zeros((X_train.shape[0], len(models)))
X_test_stack = np.zeros((X_test.shape[0], len(models)))
10折stacking
n_folds = 10
kf = KFold(n_splits=n_folds)
for i, model in enumerate(models):
X_stack_test_n = np.zeros((X_test.shape[0], n_folds))
for j, (train_index, test_index) in enumerate(kf.split(X_train)):tr_x = X_train[train_index]tr_y = y_train[train_index]model.fit(tr_x, tr_y)# 生成stacking训练数据集X_train_stack[test_index, i] = model.predict(X_train[test_index])X_stack_test_n[:, j] = model.predict(X_test)# 生成stacking测试数据集
X_test_stack[:, i] = X_stack_test_n.mean(axis=1)
model_second = LinearRegression()
model_second.fit(X_train_stack,y_train)
pred = model_second.predict(X_test_stack)
print(“R2:”, r2_score(y_test, pred))
GBDT
model_1 = models[0]
model_1.fit(X_train,y_train)
pred_1 = model_1.predict(X_test)
print(“R2:”, r2_score(y_test, pred_1))
RF
model_2 = models[1]
model_2.fit(X_train, y_train)
pred_2 = model_2.predict(X_test)
print(“R2:”, r2_score(y_test, pred_2))
ET
model_3 = models[2]
model_3.fit(X_train, y_train)
pred_3 = model_1.predict(X_test)
print(“R2:”, r2_score(y_test, pred_3))
ADA
model_4 = models[3]
model_4.fit(X_train, y_train)
pred_4 = model_4.predict(X_test)
print(“R2:”, r2_score(y_test, pred_4))
stacking：stacking是一种分层模型集成框架。以两层为例，第一层由多个基学习器组成，其输入为原始训练集，第二层的模型则是以第一层基学习器的输出作为特征加入训练集进行再训练，从而得到完整的stacking模型。stacking的方法在各大数据挖掘比赛上都很风靡，模型融合之后能够小幅度的提高模型的预测准确度。
