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 or undersampling.

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 specific use case.

Can you suggest some algorithms that help with this?

Sure! Algorithms like Random Forest, Support Vector Machines, and Gradient Boosting can help improve both precision and recall.

How do I evaluate the performance of my model?

You can use metrics like the confusion matrix, F1 score, precision-recall curves, and ROC-AUC to evaluate your model's performance.

What are some common pitfalls to avoid?

Common pitfalls include overfitting, ignoring class imbalance, and not validating your model properly. Always test on unseen data.