Is it compulsary to normalize the dataset if doing so can negatively... —...

cafe_carl

I am using raw data set with 4 feature variables (Total Cholesterol, Systolic Blood Pressure, Diastolic Blood Pressure, and Cigraeette count) to do a Binominal Classification (find stroke likelihood) using Logistic Regression Algorithm. I made sure that the class counts are balanced. i.e., an equal number of occurrences per class. using Python + sklearn, the problem is that the classification performance gets very negatively-impacted when I try...

Originally asked by GYSHIDO on Stack Exchange (ai) (CC BY-SA 4.0).

Variant 69.

Variant 1014.

uphilltech

Great question — answering as the UphillTech team:

I found the answer to my question, went back to the Python script and in the command that fits the model i.e. LR = LogisticRegression (C=0.1, solver = "sag",max_iter=1000).fit (X_train, y_train) The parameter C was set to 0.001 which is a very small value (meaning lambda is very high as C=1/lambda) (C is the regularization strength and smaller values indicate stronger regularization). more on that matter can be found here and here

Full answer by GYSHIDO: https://ai.stackexchange.com/a/15453 (CC BY-SA 4.0).

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