What are some real-world examples of loss functions in machine learning?

What are some real-world examples of loss functions in machine learning?

Loss functions are crucial in training machine learning models. For instance, Mean Squared Error (MSE) is commonly used in regression tasks to measure the average squared difference between predicted and actual values.

Can you give me more examples?

Sure! Another example is Cross-Entropy Loss, often used in classification problems to quantify the difference between predicted probabilities and actual class labels.

What about in neural networks?

In neural networks, loss functions like Hinge Loss are used for Support Vector Machines, while Categorical Cross-Entropy is popular for multi-class classification tasks.

How do these loss functions affect model performance?

The choice of loss function can significantly impact model performance, as it guides the optimization process. A well-chosen loss function can lead to better convergence and more accurate predictions.