using sklearn class weight to increase number of positive guesses in extremely unbalanced data set? Hi I have a poorly correlated and unbalanced data set I have to work with. The set is 2 classes, 0 has 96,000 values and 1 has about 200. When I run random forest or other methods I get an output like: 

    precision    recall  f1-score   support

          0       1.00      1.00      1.00     38300
          1       1.00      0.01      0.02        90

avg / total       1.00      1.00      1.00     38390


Precision is very high but it only classified one row as positive?

I tried using {class_weight = 'balanced'} in the random forest parameters and it provides:

   micro avg       1.00      1.00      1.00     38390
   macro avg       1.00      0.51      0.51     38390
weighted avg       1.00      1.00      1.00     38390


But still not many positive guesses? Should I look into oversampling?
You can try to compute class weights and assign these values to model via weight classes function. One more reminder about weights; probably major classes weight will be less than 1 so you need to round it to 1 otherwise model won't learn major class this time.