作业：利用XGBoost实现对鸢尾花数据集（Iris.csv）的分类预测

￡神魔★判官ぃ 2023-03-02 11:00 61阅读 0赞

[数据集][Link 1]  
提取码：krry

*  前4/5作为训练集，后1/5作为测试集，分割数据

data = pd.read_csv('ensemble/Iris.csv')
    #前4/5作为训练集，后1/5作为测试集
    data_training = data[0:int(len(data)*4/5)]
    data_test = data[int(len(data)*4/5):len(data)]
    #分割
    train_x = np.array(data_training.iloc[:, [i for i in range(data_training.shape[1]-1)]])
    train_y = np.array(data_training['Species'])
    test_x = np.array(data_test.iloc[:, [i for i in range(data_test.shape[1]-1)]])
    test_y = np.array(data_test['Species'])

*  训练模型

clf=XGBClassifier(base_score=0.5, booster='gbtree', learning_rate=0.05, max_depth=8, n_estimators=50)
    clf.fit(train_x, train_y)

*  测试

print(clf.score(test_x, test_y))

*  完整代码

from xgboost import XGBClassifier
    import pandas as pd
    import numpy as np
    
    def load_data():
        data = pd.read_csv('ensemble/Iris.csv')
        #前4/5作为训练集，后1/5作为测试集
        data_training = data[0:int(len(data)*4/5)]
        data_test = data[int(len(data)*4/5):len(data)]
        #分割
        train_x = np.array(data_training.iloc[:, [i for i in range(data_training.shape[1]-1)]])
        train_y = np.array(data_training['Species'])
        test_x = np.array(data_test.iloc[:, [i for i in range(data_test.shape[1]-1)]])
        test_y = np.array(data_test['Species'])
    
        return train_x, train_y, test_x, test_y
    
    
    def XGBoost():
        train_x, train_y, test_x, test_y = load_data()
        #训练
        clf=XGBClassifier(base_score=0.5, booster='gbtree', learning_rate=0.05, max_depth=8, n_estimators=50)
        clf.fit(train_x, train_y)
        #测试
        print(clf.score(test_x, test_y))
    
    
    if __name__ == '__main__':
        XGBoost()

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