网上有人用statsmodels写了一个向前逐步回归的工具,具体网址见https://planspace.org/20150423-forward_selection_with_statsmodels/。我试了一下,速度还不错,比我用sklearn写的要好。具体代码如下: importstatsmodels.formula.apiassmfimportpandasaspddefforward_selected(da...
Automated Stepwise Backward and Forward Selection This script is about an automated stepwise backward and forward feature selection. You can easily apply on Dataframes. Functions returns not only the final features but also elimination iterations, so you can track what exactly happend at the iterations...
'SSE','model'])foriinrange(1,len(x.columns)+1):tic=time.time()result=[]forcomboinitertools.combinations(x_const.columns.drop('const'),i):#因为这里的x有const存在要先drop掉,然后在后面给每一个自变量名称组合加上combo=tuple(['const'])+comboregr=sm.OLS(y,x_const[list(combo...
os.chdir('D:/Python/rollingRegression') # Windows path class featureSelection(): ''' 多元线性回归特征选择类。 参数 --- random_state : int,默认是None 随机种子。 属性 --- elasticnet_rs_best : model 弹性网络随机搜索最佳模型。 elasticnet_rs_feat_selected_ : dataframe 弹性网络随机搜索最佳模型...
os.chdir('D:/Python/rollingRegression') # Windows path class featureSelection(): ''' 多元线性回归特征选择类。 参数 --- random_state : int,默认是None 随机种子。 属性 --- elasticnet_rs_best : model 弹性网络随机搜索最佳模型。 elasticnet_rs_feat_selected_ : dataframe 弹性...
sklearn.feature_selection.VarianceThreshold 我们先定义方差的计算方法 from sklearn.feature_selection import VarianceThreshold X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]] sel = VarianceThreshold(threshold=(.8 * (1 - .8))) ...
Best Subset Selection, Forward Stepwise, Backward Stepwise Classes in sk-learn style. This package is compatible to sklearn. Examples onPipelineandGridSearchCVare given. ForwardStepwiseOLS 2020-04-19 Hyperparameter fK: at mostfKnumber of features are selected ...
Python实现逐步回归 逐步回归(Stepwise Regression)是一种逐步选择变量的回归方法,用于确定最佳的预测模型。它通过逐步添加和删除变量来优化模型的预测能力。...二、实现逐步回归的函数参数详解 实现逐步回归,可以使用toad库中的toad.selection.stepwise函数,该函数的调用方法、主要参数及其解释如下: import toad toad.select...
were valid for them, has been described as a scandal.Widespread incorrect usage and the availability of alternatives such as ensemble learning, leaving all variables in the model, or using expert judgement to identify relevant variables have led to calls to totally avoid stepwise model selection. ...
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