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How does logistic regression deal with multicollinearity?
How to Deal with Multicollinearity
- Remove some of the highly correlated independent variables.
- Linearly combine the independent variables, such as adding them together.
- Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
Does regularization remove multicollinearity?
To reduce multicollinearity we can use regularization that means to keep all the features but reducing the magnitude of the coefficients of the model. This is a good solution when each predictor contributes to predict the dependent variable.
Why is multicollinearity an issue when doing stepwise logistic regression?
In variable selection, if both contribute equally to fitting your data sample there’s a chance that you will miss both in your model if they individually are “less significant” than other non-correlated predictors (which happened to have high coefficient magnitudes for your sample) and thus were left out of your stepwise selection.
Can a regression model have severe multicollinearity?
You can have a model with severe multicollinearity and yet some variables in the model can be completely unaffected. The regression example with multicollinearity that I work through later on illustrates these problems in action. Do I Have to Fix Multicollinearity?
Is there anything inherent in AIC that can address multicollinearity?
My next question is related to Akaike’s Information Criterion, and whether there is anything inherent in AIC that can address multicollinearity? Does the selection of a model with a good AIC (or AICc) inherently lead the user to select a model in which there is an acceptable level (ie not too much) of multicollinearity?
What kind of problems are caused by multicollinearity?
Multicollinearity causes the following two basic types of problems: The coefficient estimates can swing wildly based on which other independent variables are in the model. The coefficients become very sensitive to small changes in the model.