引导聚合算法（装袋；bagging）的python实现

# 输入csv格式的数据集，输出模型的平均准确率



import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import BaggingClassifier


if __name__ == '__main__':
    dataset = np.array(pd.read_csv("sonar.csv", sep=',', header=None))
    k_Cross = KFold(n_splits=8, random_state=0, shuffle=True)
    index = 0
    score = np.array([])
    data,label = dataset[:,:-1],dataset[:,-1]
    for train_index, test_index in k_Cross.split(dataset):
        train_data, train_label = data[train_index, :], label[train_index]
        test_data, test_label = data[test_index, :], label[test_index]
        tree = DecisionTreeClassifier()
        model = BaggingClassifier(base_estimator=tree, n_estimators=500, max_samples=1.0, max_features=1.0,
                                  bootstrap=True, random_state=1)
        model.fit(train_data, train_label)
        pred = model.predict(test_data)
        acc = accuracy_score(test_label, pred) * 100
        score = np.append(score,acc)
        print('score[{}] = {}%'.format(index,acc))
        index+=1
    print('mean_accuracy = {}%'.format(np.mean(score)))