stacking集成模型示例如下：

stacking一般由2层堆叠构成

Stacking集成算法思路

上图为整体流程，思路如下：

1. 把原始数据切分成两部分：训练集D-train与测试集D-test，训练集部分用来训练整体的Stacking集成模型，测试集部分用来测试集成模型

2. 训练集D-train中又划分出两个部分：Training folds-训练集与Validation fold-验证集，其中Training folds部分用来训练初级学习器（浅黄色的模型）

3. 下图中的Learn对应上图Training folds，用来训练初级学习器；下图中的Predict对应上图Validation fold，用来通过初级训练器得到预测结果Predictions，这些预测结果将用来训练次级学习器Model2

4. Model2一般是逻辑回归，用来计算各个初级学习器的权重。

5. 这一整套训练完成后，用D-test来测试整个集成模型，得到模型的指标

代码示例

# _*_coding:utf-8 _*_
# Time: 2022/3/29
"""

"""
from sklearn.ensemble import StackingClassifier
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


def load_data(samples=1000):
    """
    用来生成训练、测试数据
    :param samples: 数据量
    :return: 返回x与y或切分训练集后的x与y
    """
    data_x, data_y = make_classification(n_samples=samples, n_classes=4, n_features=10, n_informative=8)
    df_x = pd.DataFrame(data_x, columns=['f_1', 'f_2', 'f_3', 'f_4', 'f_5', 'f_6', "f_7", "f_8", "f_9", "f_10"])
    df_y = pd.Series(data_y)
    x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, train_size=0.7, random_state=0, shuffle=True)
    return x_train, x_test, y_train, y_test


def main():
    x_train, x_test, y_train, y_test = load_data()
    stacking_classifier = StackingClassifier(
        estimators=[ # 初级学习器
            ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
            ('svr', make_pipeline(StandardScaler(), SVC(random_state=42)))
        ],
        final_estimator=LogisticRegression()) # 次级学习器
    stacking_classifier.fit(x_train, y_train)
    result_prediction = stacking_classifier.predict(x_test)
    acc = accuracy_score(y_test, result_prediction)  # 准确率
    print("acc:", acc)


if __name__ == '__main__':
    main()