Introduction Of Ensemble Learning
June 8, 2019
什么是集成学习
集成学习是使用一系列学习器进行学习，并使用某种规则把各个学习结果进行整合从而获得比单个学习器更好的学习效果的一种机器学习方法。
类似于生活中的专家会诊等。
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()
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
三种算法分别训练
from sklearn.linear_model import LogisticRegression
log_clf = LogisticRegression(solver='lbfgs')
log_clf.fit(X_train, y_train)
log_clf.score(X_test, y_test)
0.864
from sklearn.svm import SVC
svc_clf = SVC(gamma=1.0)
svc_clf.fit(X_train, y_train)
svc_clf.score(X_test, y_test)
0.888
from sklearn.tree import DecisionTreeClassifier
dt_clf = DecisionTreeClassifier()
dt_clf.fit(X_train, y_train)
dt_clf.score(X_test, y_test)
0.888
进行投票
y_log_predict = log_clf.predict(X_test)
y_svc_predict = svc_clf.predict(X_test)
y_dt_predict = dt_clf.predict(X_test)
y_predict = np.array((y_log_predict + y_svc_predict + y_dt_predict)>=2, dtype=int)
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_predict)
0.912
scikit-learn 中的 VotingClassifier
from sklearn.ensemble import VotingClassifier
v_clf = VotingClassifier(estimators=[
("log", LogisticRegression(solver='lbfgs')),
("svc", SVC(gamma=1.0)),
("dt", DecisionTreeClassifier())
], voting='hard')
v_clf.fit(X_train, y_train)
v_clf.score(X_test, y_test)
0.912