print("Selected features:", best_features) 五、双向逐步回归(Stepwise Regression) 双向逐步回归结合了前向选择和后向消除,每次添加或删除特征,使得模型的表现得到最大提升。下面是双向逐步回归的实现代码: def stepwise_selection(X, y, significance_level=0.05): initial_features = X.columns.tolist() best_f...
结合向前选择和向后移除的方法,可以进一步优化模型。这种方法被称为逐步回归(Stepwise Regression)。 def stepwise_selection(data, target, significance_level=0.05): initial_features = data.columns.tolist() best_features = [] while len(initial_features) > 0: remaining_features = list(set(initial_feature...
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...
# 定义逐步回归函数defstepwise_selection(X,y,significance_level=0.05):initial_features=X.columns.tolist()best_features=[]whilelen(initial_features)>0:# 向模型添加每一个变量,并寻找最佳模型changed=Falseforfeatureininitial_features:temp_features=best_features+[feature]X_temp=X[temp_features]X_temp=a...
defstepwise_selection(data,response,significance_level=0.05):initial_features=data.columns.tolist()initial_features.remove(response)selected_features=[]whilelen(initial_features)>0:best_pval=1best_feature=Noneforfeatureininitial_features:formula=f"{response}~{' + '.join(selected_features+[feature])}...
def stepwise_selection(X, y, initial_list=[],threshold_in=0.01,threshold_out = 0.05, verbose = True): ''' threshold_out为t检验,threshold_in为F检验 X:待筛选变量 y:好坏标签 ''' included = list(initial_list) while True: changed=False excluded = list(set(X.columns)-set(included)) new...
逐步回归函数定义:stepwise_selection函数接受特征矩阵X、目标变量y、初始特征列表initial_list(默认为空)、进入模型的显著性水平threshold_in和移除模型的显著性水平threshold_out。 前向步骤:在每一步中,我们计算剩余特征中每个特征的p值,并选择p值最小的特征(如果其p值小于threshold_in)添加到模型中。 后向步骤:然...
1 return w向后逐步：f_test_confidence_interval: F检验的置信度def _backward_selection(self, ...
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...
可以发现双向逐步回归挑选出了12个入模变量。 3 向前筛选逐步回归实现 接着用向前筛选的方法进行逐步回归变量挑选,具体代码如下: final_data = toad.selection.stepwise(qz_date, target = 'Risk', estimator='ols', direction = 'forward', criterion = 'aic' ) final_data 得到结果: ...