What is the difference between sample weight and class weight options in scikit learn?

Question

I have class imbalance problem and want to solve this using cost sensitive learning.

1. under sample and over sample
2. give weights to class to use a modified loss function

Question

Scikit learn has 2 options called class weights and sample weights. Is sample weight actually doing option 2) and class weight options 1). Is option 2) the the recommended way of handling class imbalance.

Answer 1

It's similar concepts, but with sample_weights you can force estimator to pay more attention on some samples, and with class_weights you can force estimator to learn with attention to some particular class. sample_weight=0 or class_weight=0 basically means that estimator doesn't need to take into consideration such samples/classes in learning process at all. Thus classifier (for example) will never predict some class if class_weight = 0 for this class. If some sample_weight/class_weight bigger than sample_weight/class_weight on other samples/classes - estimator will try to minimize error on that samples/classes in the first place. You can use user-defined sample_weights and class_weights simultaneously.

If you want to undersample/oversample your training set with simple cloning/removing - this will be equal to increasing/decreasing of corresponding sample_weights/class_weights.

In more complex cases you can also try artificially generate samples, with techniques like SMOTE.