https://blog.csdn.net/xunan003/article/details/100106478 一、Hard Voting 与 Soft Voting 的对比1)使用方式
2)思想
二、各分类算法的概率计算
1)逻辑回归算法
2)kNN 算法
3)决策树算法
4)SVM 算法
三、scikit-learn 中使用集成分类器:VotingClassifier1)模拟数据集 import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42 2)voting = 'hard':使用 Hard Voting 做决策 from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import VotingClassifier
# 实例化
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)
# 准确率:0.896 3)voting = 'soft':使用 Soft Voting 做决策 voting_clf = VotingClassifier(estimators=[ ('log_clf', LogisticRegression()), ('svm_clf', SVC(probability=True)), ('dt_clf', DecisionTreeClassifier(random_state=666)) ], voting='soft') voting_clf.fit(X_train, y_train) voting_clf.score(X_test, y_test) # 准确率:0.912 |
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