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:
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
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)
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
Model2
一般是逻辑回归,用来计算各个初级学习器的权重。
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: /3/29""""""from sklearn.ensemble import StackingClassifierimport pandas as pdfrom sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.svm import SVCfrom sklearn.pipeline import make_pipelinefrom sklearn.preprocessing import StandardScalerfrom sklearn.linear_model import LogisticRegressionfrom sklearn.metrics import accuracy_scoredef 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_testdef 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()
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