playing with [this]( dataset, trying to do multiclass classification on `Target`(3 possible values, `Dropout Enrolled Graduated`) I'm find that models have a hard time discerning `Enrolled`: (using `SVC` as example) ```py -----SVC----- precision recall f1-score support Dropout 0.84 0.73 0.78 284 Enrolled 0.52 0.33 0.40 159 Graduate 0.77 0.94 0.84 442 ```the data's imbalanced, ``` >>> ALL['Target'].value_counts() Graduate 2209 Dropout 1421 Enrolled 794 ```I tried `class_weight='balanced'` on SVC which improved it a little (`f1` of `Enrolled` sits around `0.47`), then I tried down sampling to get ```py down_sampled = pd.concat([ ALL.loc[ALL['Target'] == 'Graduate'].iloc[:800], ALL.loc[ALL['Target'] == 'Dropout'].iloc[:800], ALL[ALL['Target'] == 'Enrolled'].iloc[:800]]) -----SVC----- precision recall f1-score support Dropout 0.78 0.67 0.72 200 Enrolled 0.62 0.66 0.64 199 Graduate 0.75 0.81 0.78 200 ```which (I think) made it overall better, but it's still having a hard time classifying `Enrolled`, so I think the bad performance isn't *only* due to imbalance are there any other techniques I can use to improve this?
Can you make a confusion matrix? This is the case where it shines