How to Measure the difference between features in dataframe?
Aside from using the coefficients of the support vectors from your model, you could try build other models.
A decision tree approach will explicitly show you which input features split the data - those nearer the root being more important, for some definition of important.
If you try a feature reduction technique, like PCA, and rebuild you model, the coefficients of the components here will tell you which contribute most.
Or you could be completely thug-headed, and build lots of models leaving out some features and see which are better.
Or you could be lateral, and consider what's so different about the few points that your model doesn't accurately classify.
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?