X-Validation and Performance operators - % of accuracy from each feature
I have a feature set of 100 features for 200 input samples. I am using Rapid miner as follows:
Main Process: Feature Vector-> X-Validation.
Training: Logistic Regression operator / Decision tree
Testing : Apply model --> Performance
Features are Entropy, edgedensity, etc of 200 image samples. Labels are Class 1 and Class 2 in Text. Feature values are decimal numbers.
I would like to know how I can find the accuracy of individual feature (100 features) towards the performance accuracy of classifying Class 1 and Class 2 (E.g: How much accuracy does entropy gives in classification, how much accuracy does edge density gives in classification etc.). Also how I can find which all features contribute the most maximum towards the overall accuracy result.