What are the differences between various loss functions in machine learning?

What are the differences between various loss functions in machine learning?

Loss functions are crucial in machine learning as they measure how well a model's predictions match the actual outcomes. Common loss functions include Mean Squared Error (MSE), Cross-Entropy Loss, and Hinge Loss, each serving different types of problems.

Can you explain Mean Squared Error and when to use it?

Mean Squared Error (MSE) is used primarily for regression tasks. It calculates the average of the squares of the errors, which penalizes larger errors more than smaller ones. It's ideal when you want to minimize the impact of outliers.

What about Cross-Entropy Loss?

Cross-Entropy Loss is commonly used for classification tasks. It measures the difference between two probability distributions - the true distribution and the predicted distribution. It's particularly effective for multi-class classification problems.

Are there any scenarios where Hinge Loss is preferred?

Yes, Hinge Loss is often used in Support Vector Machines (SVMs) for 'maximum-margin' classification. It focuses on the margin between classes, making it suitable for binary classification tasks.