机器学习系列（十二） 集成学习 2020.6.13

前言

本节学习集成学习

1、原理

集成学习

• 几种机器学习算法都跑一遍
• 少数服从多数决定结果

scikit中

• hard：一人一票
• soft：有权重，每种算法做出结果的概率

差异性

• 每个子模型只看样本数据一部分
• 每个子模型不需要太高的准确率
• bagging：放回取样
• pasting：不放回取样

随机森林

• 决策树在节点划分上，在随机的特征子集上寻找最优划分特征

2、实现

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

"""集成学习"""
# 数据
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()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# 逻辑回归
log_clf = LogisticRegression()
log_clf.fit(X_train, y_train)
print(log_clf.score(X_test, y_test))
# SVM
svm_clf = SVC()
svm_clf.fit(X_train, y_train)
print(svm_clf.score(X_test, y_test))
# 决策树
dt_clf = DecisionTreeClassifier(random_state=666)
dt_clf.fit(X_train, y_train)
print(dt_clf.score(X_test, y_test))
# 集成学习（投票）
y_predict1 = log_clf.predict(X_test)
y_predict2 = svm_clf.predict(X_test)
y_predict3 = dt_clf.predict(X_test)
y_predict = np.array((y_predict1 + y_predict2 + y_predict3) >= 2, dtype='int') #少数服从多数
print(accuracy_score(y_test, y_predict))

"""使用scikit库实现集成学习"""
from sklearn.ensemble import VotingClassifier
# 使用 Hard Voting Classifier
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)
print(voting_clf.score(X_test, y_test))
# 使用 Soft Voting Classifier
voting_clf2 = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()),
    ('svm_clf', SVC(probability=True)), #计算结果概率
    ('dt_clf', DecisionTreeClassifier(random_state=666))],
                             voting='soft')
voting_clf2.fit(X_train, y_train)
print(voting_clf2.score(X_test, y_test))
# 使用 bagging
from sklearn.ensemble import BaggingClassifier
bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                           n_estimators=500, max_samples=100,
                           bootstrap=True) #bootstrap决定取样后放不放回
bagging_clf.fit(X_train, y_train)
print(bagging_clf.score(X_test, y_test))
# oob和n_jobs
bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True,
                               n_jobs=-1) #oob保证所有样本都能被抽取到，n_jobs进行并行运算
bagging_clf.fit(X, y)
print(bagging_clf.oob_score_)
# 对特征随机采样
random_patches_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True,
                               max_features=1, bootstrap_features=True)
random_patches_clf.fit(X, y)
print(random_patches_clf.oob_score_)

"""随机森林"""
from sklearn.ensemble import RandomForestClassifier
rf_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, oob_score=True, random_state=666, n_jobs=-1) #随机森林拥有决策树和BaggingClassifier的所有参数
rf_clf.fit(X, y)
print(rf_clf.oob_score_)

结语

传统的机器学习
大多过了一遍

后续会继续深入学习