I am working on multiclass-imbalanced data. My dependent variable is highly skewed.
Injury 2(No Injury) 208753 1(Medium Injury) 22318 0(severe Injury) 3394
I have used random forest algorithm with parameter "class_weight='balanced' " to manage the class 2 imbalance.
I get the below results when I use average='micro'.
[[ 34 107 688] [ 148 778 4592] [ 905 4635 46730]] Accuracy Score: 0.8110616374089428 precision score: 0.8110616374089428 Recall score: 0.8110616374089428 AUC Score: 0.8582962280567071 F1 score: 0.8110616374089428 Kappa Score: 0.05522284663052324
For the average = 'macro', the results are below.
[[ 31 125 684] [ 157 838 4559] [ 890 4694 46639]] Accuracy Score: 0.8104816009007626 precision score: 0.3586119227436326 Recall score: 0.3602869806251181 AUC Score: 0.5253225798824679 F1 score: 0.3592735337079687 Kappa Score: 0.06376296115668922
So, which results should I consider to evaluate the model? If I have to consider the macro, then my model performance is really bad. Please suggest if there are any methods to improve the precision, recall and AUC score?
If I consider micro results, my precision, recall, f1 score is same. How can I justify this in the project?
I am interested in medium and severe injuries. But individual class precisions 0.0312(severe), 0.1409(medium) for macro are very low. The overall precision score is also very low 0.35. Is it possible to increase these scores? Or Is it fine to consider these low values as my final results for the project? Any suggestions are greatly appreciated. NOTE: I have tried SMOTE oversampling and ensemble cross-validation with different algorithms but I ended up having precision and recall scores less than 50.
Any help would be appreciated.