支持向量机-递归特征消除算法（SVM-RFE）

import numpy as np from sklearn.feature_selection import RFE, RFECV from sklearn.svm import SVC, SVR from sklearn.svm import LinearSVC from sklearn import model_selection import pandas as pd df = pd.read_csv('E:\Pycharm\PycharmLearning\RF\\RFEtestyu1012.csv', encoding='gbk') print(df.head()) df.label=df.label.astype(str) #df.label=df.label.astype(str) y=df.label print(y.head()) x=df.drop('label', axis=1) print(x.head()) features = x print('开始训练特征') svc = SVC(kernel="linear", C=1) rfe = RFE(estimator=svc, n_features_to_select=1, step=1) rfe.fit(x,y) print('训练特征选择完成，输出特征：') result = features.columns[rfe.get_support()] print(result) print(rfe.ranking_) estimator1 = SVR(kernel='linear') re = RFECV(estimator=estimator1, step=1, cv=10) re.fit(x,y) print('特征输出完毕')