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资料：

回归模型估测：

import statsmodels.api as smX2 = sm.add_constant(X)est = sm.OLS(Y, X2)est2 = est.fit()print(est2.summary())

predictorcols = ['age', 'area', 'room', 'l_room', 'total_floor', '东南向', '东向','南北向', '南向', '西向', '中', '低']import itertoolsAICs = {}for k in range(1, len(predictorcols)+1):    for variables in itertools.combinations(predictorcols, k):        predictor1 = X[list(variables)]        predictor2 = sm.add_constant(predictor1)        est = sm.OLS(Y, predictor2)        res = est.fit()        AICs[variables] = res.aic

from collections import Counterc = Counter(AICs)c.most_common()[::-10]

比全部抛进去准确了 6W