15. Python零基础学习第15课 MultiCollinearity In Linear Regression多重共线性及VIF是python机器学习&深度学习&数据分析,适合初学者的教程,119集学完即可操作!的第15集视频,该合集共计95集,视频收藏或关注UP主,及时了解更多相关视频内容。
下面的定义函数中直接调用的variance_inflation_factor函数计算的VIF,得到的结果是不对的。 defcheckVIF(df):fromstatsmodels.stats.outliers_influenceimportvariance_inflation_factorname=df.columnsx=np.matrix(df)VIF_list=[variance_inflation_factor(x,i)foriinrange(x.shape[1])]VIF=pd.DataFrame({'feature':...
fromstatsmodels.stats.outliers_influenceimportvariance_inflation_factor# 创建包含自变量和目标变量的数据框,去掉目标变量YX=df[['X1','X2','X3']]# 计算VIF值vif_data=pd.DataFrame()vif_data["Feature"]=X.columns vif_data["VIF"]=[variance_inflation_factor(X.values,i)foriinrange(X.shape[1])]pr...
#VIF检验与多重共线性:Python实现详解 ## 引言 在进行多元线性回归分析时,一个常见的问题就是多重共线性(Multicollinearity)。多重共线性是指自变量之间存在高度相关性,这可能导致回归模型的系数不稳定、标准误较大,从而影响模型的解释性和预测能力。近年来,VIF(方差膨胀因子)作为检测多重共线性的一种有效工具,得到...
import pandas as pd from statsmodels.stats.outliers_influence import variance_inflation_factor def calculate_vif(data): vif_data = pd.DataFrame() vif_data["Feature"] = data.columns vif_data["VIF"] = [variance_inflation_factor(data.values, i) for i in range(data.shape[1])] return vif...
['RD_Spend', 'Marketing_Spend']]) vif...= pd.DataFrame() vif["Ficture"] = X.columns vif["Fctor"] = [variance_inflation_factor(X.values,i) for...i in range(X.shape[1])] print(vif) 执行结果如下,如上结果所示,两个自变量对应的方差膨胀因子均低于10,说明构 建模型的数据并不存在多...
for i in range(X.shape[1])], index=X.columns) print(ds) Output:Different ways to resolve the issue of Multicollinearity-Selection of Variables The variables should be selected in a way that the ones which are highly correlated are removed and we make use of only the significant variable...
It is a measure for multicollinearity of the design matrix, exog. One recommendation is that if VIF is greater than 5, then the explanatory variable given by exog_idx is highly collinear with the other explanatory variables, and the parameter estimates will have large standard errors because ...
Time will tell.1、列表生成器下面的代码会报错,为什么?class A(object): x = 1 gen = (x for _ in xrange(10)) # gen=(x for _ in range(10)) if __name__ == "__main__": print(list(A.gen))答:这个问题是变量作用域问题,在 gen=(x f ...
回答我自己的问题:我花了半天的时间尝试调试，初步的工作版本如下所示，不是很优雅，但现在它的工作是...