How multicollinearity can be hazardous to your model ?

All about Multicollinearity

In almost all popular techniques e.g. Linear Regression, Logistic Regression, Cluster Analysis, it is advised to check and remove the traces of multicollinearity. Why ??? Is it important at all, or can we skip it. How does it affect the model ?

Let's try to explore answers of all these questions ... Secret of multicollinearity revealed !

Variables Reduction Techniques - Ready Reckoner

Variables Reduction Techniques

At times during supervised learning  (such as Logistic and Linear Regression) and unsupervised learning (such as Cluster Analysis), we need to check multicollinearity in the data.

Variables should not be correlated with each other. If they are, we need to remove the redundant variables.However by Removing variables, we loose information, which we don’t want to.

 Also, if A, B and C are correlated, which of the two should be removed, is always a puzzle. So we offer you 3 techniques that would help you in variables reduction.