how did u handle multicolleanarity in logistic model

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.

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

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