Python3入门机器学习经典算法与应用——集成学习

系统管理员 2022-12-22 09:38 147阅读 0赞

# 集成学习 #

kNN
    
    逻辑回归
    
    SVM
    			---->投票，少数服从多数
    决策树
    
    神经网络
    
    贝叶斯

# 用numpy实现 #

## 什么是集成学习 ##

import numpy as np
    import matplotlib.pyplot as plt

from sklearn import datasets
    
    X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=42)

plt.scatter(X[y==0,0], X[y==0,1])
    plt.scatter(X[y==1,0], X[y==1,1])
    plt.show()

![在这里插入图片描述][20201119141828353.png_pic_center]

from sklearn.model_selection import train_test_split
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# 逻辑回归预测
    from sklearn.linear_model import LogisticRegression
    
    log_clf = LogisticRegression()
    log_clf.fit(X_train, y_train)
    log_clf.score(X_test, y_test)

0.864

# SVM预测
    from sklearn.svm import SVC
    
    svm_clf = SVC()
    svm_clf.fit(X_train, y_train)
    svm_clf.score(X_test, y_test)

0.896

# 决策树预测
    from sklearn.tree import DecisionTreeClassifier
    
    dt_clf = DecisionTreeClassifier(random_state=666)
    dt_clf.fit(X_train, y_train)
    dt_clf.score(X_test, y_test)

0.864

# 结果
    y_predict1 = log_clf.predict(X_test)
    y_predict2 = svm_clf.predict(X_test)
    y_predict3 = dt_clf.predict(X_test)

# 投票得到最终结果，大于等于2，结果为1，否则为0
    y_predict = np.array((y_predict1 + y_predict2 + y_predict3) >= 2, dtype='int')

y_predict[:10]

array([1, 0, 0, 1, 1, 1, 0, 0, 0, 0])

from sklearn.metrics import accuracy_score
    
    # 最终的准确率
    accuracy_score(y_test, y_predict)

0.904

### 使用Voting Classifier ###

from sklearn.ensemble import VotingClassifier
    
    voting_clf = VotingClassifier(estimators=[
        ('log_clf', LogisticRegression()), 
        ('svm_clf', SVC()),
        ('dt_clf', DecisionTreeClassifier(random_state=666))],
                                 voting='hard')

voting_clf.fit(X_train, y_train)
    voting_clf.score(X_test, y_test)

0.904

[20201119141828353.png_pic_center]: /images/20221120/7139ddf64c564d89be7be0611ed11c4b.png