如何用几行代码做特征选择

from sklearn.feature_selection import RFEfrom sklearn.linear_model import LinearRegression#Load boston housing dataset as an exampleX = np.array(train1[feature_use].fillna(-1))[1:train1.size,:]Y = np.array(train1['target'])[1:train1.size]#print(X)#print(Y)names = feature_use#use linear regression as the modellr = LinearRegression()#rank all features, i.e continue the elimination until the last onerfe = RFE(lr, n_features_to_select=1)rfe.fit(X,Y)print("Features sorted by their score:")#print(sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), names),             reverse=True))sortedlist = sorted(zip(map(lambda x: round(x, 4), rfe.ranking_), names),             reverse=True)print(sortedlist)feature_use = []for index in sortedlist[len(sortedlist)-70 : ]:    if index[0]>0:        feature_use.append(index[1])print(feature_use)

上面的X为数据集的特征集合 Y为标签集合
在sortlist里对特征的重要性进行了排序

最近做机器学习的一点感悟是，特征的影响远比模型参数来的大，特征是现实世界在算法中的倒影。
在特征工程中要对业务有非常深的理解，强调返璞归真，删除无效特征，减少引起干扰的特征。
加特征的过程需要一个一个来，还要多思考这些特征之间的关系，是否是强烈线性相关的。

# random forest select features'''from sklearn.ensemble import RandomForestRegressorimport numpy as np#Load boston housing dataset as an exampleX = np.array(train1[feature_use].fillna(-1))[1:train1.size,:]Y = np.array(train1['target'])[1:train1.size]print(X)print(Y)names = feature_userf = RandomForestRegressor()rf.fit(X, Y)print("Features sorted by their score:")print(sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), names),             reverse=True))'''