How can I improve precision and recall in my model?

How can I improve precision and recall in my model?

To improve precision and recall, you can adjust the classification threshold, use different algorithms, or apply techniques like oversampling.

What is the trade-off between precision and recall?

The trade-off is that increasing precision often decreases recall and vice versa. You need to find a balance based on your goals.

Are there specific metrics I should monitor?

Yes, you should monitor precision, recall, F1 score, and the ROC-AUC curve to evaluate your model's performance.

Can I use cross-validation to help with this?

Absolutely! Cross-validation helps ensure that your model's performance is consistent across different subsets of data.

What techniques can I use to handle class imbalance?

You can use techniques like SMOTE, ADASYN, or simply adjust class weights in your model to handle class imbalance effectively.