What are thresholds in classification models?

What are thresholds in classification models?

Thresholds in classification models determine the point at which a predicted probability is converted into a class label.

How do I choose the right threshold for my model?

Choosing the right threshold often involves analyzing the trade-off between precision and recall, or using ROC curves.

Can thresholds affect model performance?

Yes, the chosen threshold can significantly impact performance metrics like accuracy, precision, and recall.

What is the default threshold used in most models?

The default threshold is usually set at 0.5, meaning if the predicted probability is above 0.5, the instance is classified as positive.