How do thresholds affect the performance of machine learning models?

How do thresholds affect the performance of machine learning models?

Thresholds play a crucial role in determining the sensitivity and specificity of a model. By adjusting the threshold, you can influence the trade-off between false positives and false negatives.

Can you explain what sensitivity and specificity mean?

Sure! Sensitivity measures the proportion of actual positives correctly identified, while specificity measures the proportion of actual negatives correctly identified.

What happens if I set the threshold too high?

If the threshold is set too high, the model may miss many positive cases, leading to a high number of false negatives.

And what about setting it too low?

Setting the threshold too low can result in a high number of false positives, meaning the model incorrectly identifies negatives as positives.

How can I find the optimal threshold for my model?

You can use techniques like ROC curves or precision-recall curves to visualize performance at different thresholds and choose the optimal one based on your specific needs.