机器学习集成模型ML ens学习——多层模型集成（一）

集成模型就是把多个模型融合在一起使用，通过构建一层层的模型体系，最终得到不同模型的预测结果

首先安装：pip install mlens

案例

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from mlens.ensemble import SuperLearner
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression

if __name__ == '__main__':
    # ============== 准备数据 ==============
    x, y = make_classification(n_samples=10000, n_classes=4, n_informative=5)
    x = MinMaxScaler().fit_transform(x)
    xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.3, random_state=100)

    # ============== 搭建集成模型的结构 ==============
    ensemble = SuperLearner(scorer=accuracy_score, random_state=0, verbose=2)
    ensemble.add([KNeighborsClassifier(),  # 第一层
                  RandomForestClassifier()])
    ensemble.add_meta(LogisticRegression())  # 最后使用这个模型统一预测结果

    ensemble.fit(xtrain, ytrain)

    # ============== 开始预测 ==============
    preds = ensemble.predict(xtest)
    print(pd.DataFrame(ensemble.data))
    print("acc：", accuracy_score(preds, ytest))

参考文章

ML ens教程：http://ml-ensemble.com/info/tutorials/start.html