机器学习mlxtend_01
# -*- coding: utf-8 -*- """ Created on Wed Oct 24 09:53:29 2018 @author: User """ import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import itertools from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from mlxtend.classifier import EnsembleVoteClassifier from mlxtend.data import iris_data from mlxtend.plotting import plot_decision_regions clf1 = LogisticRegression(random_state = 0) clf2 = RandomForestClassifier(random_state=0) clf3 = SVC(random_state = 0, probability=True) eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[2, 1, 1], voting='soft') X, y =iris_data() X=X[:, [0, 2]] gs = gridspec.GridSpec(2, 2) fig = plt.figure(figsize=(10, 8)) labels = ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble'] for clf, lab, grd in zip([clf1, clf2, clf3, eclf], labels, itertools.product([0, 1], repeat=2)): clf.fit(X, y) ax = plt.subplot(gs[grd[0], grd[1]]) fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2) plt.title(lab) plt.show()
运行结果:
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