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.
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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.
Enjoy reading our other articles and stay tuned with ....
Kindly do provide your feedback in the 'Comments' Section and share as much as possible.
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