What are the best practices for choosing loss functions in machine learning?

What are the best practices for choosing loss functions in machine learning?

Choosing the right loss function is crucial for model performance. It should align with your specific task and data characteristics.

Can you give me examples of different types of loss functions?

Sure! Common loss functions include Mean Squared Error for regression, Cross-Entropy Loss for classification, and Hinge Loss for SVMs.

How do I know which loss function to use for my project?

Consider the nature of your problem: is it regression or classification? Also, think about the distribution of your data and the desired outcome.

Are there any pitfalls to avoid when selecting a loss function?

Yes, avoid using a loss function that doesn't match your evaluation metric, and be cautious of overfitting with complex loss functions.