how did u handle multicolleanarity in logistic model

Question

Sigiloso · Accepted Answer

Quite a simple question, u can either add or drop variables; obtain a larger dataset to estimate the regression model; transform the variables ( eg. log transformation) etc.

Dr.Hanumth Sastry · Answer

Try the following:
1) Remove highly correlated predictor variables from Regression Model
2) Apply PCA (Principal Component Analysis) or LDA (Linear Discriminant Analysis) methods on data attributes
3) Choose appropriate sample size and ensure that computed VIF value is below 2

Tausif Husain · Answer

1:pca for large number of features
2: RFE with VIF
3: if dataset has less number of features then plot a heat map,find highly correlated features and drop them

Tausif Husain · Answer

1:pca for large number of features
2: RFE with VIF
3: if dataset has less number of features then plot a heat map,find highly correlated features and drop them

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