作业：利用AdaBoost对马疝病数据集（horseColic）进行分类预测

桃扇骨 2023-03-02 10:59 12阅读 0赞

[数据集][Link 1]  
提取码：krry

*  先利用pandas读入csv文件，以DataFrame形式存储；然后将数据转成list（其实也可以直接操作，不过本人习惯这样做）：  
    `data = np.array(data).tolist()`
 *  分割数据，最后一列作为标签类别y，其余列为x：

x = []; y = []
    for i in range(len(data)):
         y.append(data[i][-1])
         del data[i][-1]
         x.append(data[i])

*  训练模型

clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=7, min_samples_leaf=7), n_estimators=100, algorithm='SAMME', learning_rate=0.95)
    clf.fit(train_x, train_y)

*  测试

print(clf.score(test_x, test_y))

*  完整代码：

from sklearn.ensemble import AdaBoostClassifier
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.model_selection import cross_val_score
    import pandas as pd
    import numpy as np
    
    def load_data(path):
        data = pd.read_csv(path, sep='\t', names=[i for i in range(22)])
        data = np.array(data).tolist()
        x = []; y = []
        for i in range(len(data)):
            y.append(data[i][-1])
            del data[i][-1]
            x.append(data[i])
    
        x = np.array(x)
        y = np.array(y)
    
        return x, y
    
    def AdaBoost():
        train_x, train_y = load_data('ensemble/horseColicTraining.txt')
        test_x, test_y = load_data('ensemble/horseColicTest.txt')
        #训练
        clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=7, min_samples_leaf=7), n_estimators=100, algorithm='SAMME', learning_rate=0.95)
        clf.fit(train_x, train_y)
        #测试
        print(clf.score(test_x, test_y))
    
    
    if __name__ == '__main__':
        AdaBoost()

[Link 1]: https://pan.baidu.com/s/14PM4zLUBr6BamLA-nEFujQ