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Logistic regression with and without regularisation
I have a classification case, wherein I use Logistic regression.
At first instant - I get accuracy of 68.52% with 100% recall.
subsequently with regularisation - I get accuracy of 98.77% with 100 % recall.
1. can you elaborate , how regularisation leads to this much jump in better accuracy. can you explain the basics behind this rapidminer option.
2. I couldn't see lamda value in result. Is there any way to get it displayed.
3. Both the cases , I get 100% recall. ( zero false negative, which is desirable in my case.).
But Im not sure, whether it is a good model.
I achieved above after normalisation and cross validation. Im enclosing
both results. thanks