Skewed data with low ML accuracy

[mghasemi19]

Considering the fraction of signals versus total backgrounds, there is a high skewed data which ends up very low accuracy and F1 score for all ML models. Is there any way to fix this like: including only the main backgrounds, generating more signals, or adding more discriminating variables?

[mghasemi19]

We can probably deal with this by including less signal events rather than considering all the signals. In the first try, I am going to include 50%-50% (signal-background) events for ML training and see how the model's outputs look like.

[mghasemi19]

Cheking with LG, he suggested to generate more samples and check overfitting. I will try to check overfitting for both RF and NN optimized models and see if it affects the final model's accuracy.

[mghasemi19]

For RF and NN models, hyperparameters are tuned and overfitting is checked. Optimized NN model shows better performance in terms of accuracy and generalization to the whole dataset.