I'm a novice in machine learning-based classification techniques. Please do help
1) What is the difference between SMO (weka) and the LibSVM algorithms? Which is the best? Because the parameter requirements of the two are very different.
2) Feature reduction (e.g.PCA) and feature selection (e.g. InfoGain) are two different techniques for reducing features. Which one to rely on? In which conditions are they to be used?
3) In Infogain eval, the ranking algorithm ranks the features and the threshold parameter can remove the unwanted features with respect to entropy measure. Can we optimize both? Or do we optimize one of them alternatively? What I should I be looking for - accuracy?
4) Is accuracy the only thing that I should be looking for? Of course there is overfitting, but can I quantify the predictive power of the model other than just CV accuracy? Some other measure or technique?