How to Measure the difference between features in dataframe?
Can you try to use KS-test for your features? for example, feature 1, split by it's class. then you get two groups. Then test if they come from different distribution or just record the p-value.
when you have all the test result or p-value, make another model with the samples that comes from different distribution / very low p-value. see if the new model is better or similar.
not sure if this achieves anything.
wanted to comment but couldn't do so.
I have a dataframe with around 20000 rows and 98 features (all the features are numerical) and a target feature with binary values: 0 and 1. Basically, there are two population (first population with target value 1 --50%--, and the second with target value 0 -50%- balanced data). In a classification problem, I tried to predict the target value given the data. So, I have implanted a supervised learning algorithm (e.g., SVM) to predict the target value, and could obtain a very good result with around 0.95 accuracy. This result gives me a point that there is a considerable difference between the features. So, in the next step, I have to know what are the important features which made this difference, and what is best way to quantify this difference in the features between these two group of population. Any idea?