Hi Demet,
Since you are experiencing poor performance with "sim" on the imbalanced dataset, here are some methods you can consider to improve your model's performance:
- Increase the number of instances in the minority class by duplicating samples or using techniques like "SMOTE".
- Assign higher weights to the minority class during training to penalize misclassification more heavily, this can be used by adjusting the "Weights" parameter in "fitcensemble" function.
- If you manually implement the training loop for your neural network, you can define a custom loss function that applies higher penalties to errors in the minority class, effectively balancing the influence of each class during training.
Hope this helps!!
