Definition: A loss function quantifies the “error” between the predicted output of a machine learning model and the true target value.

Detailed Description:

Loss functions are used to evaluate the performance of a model and guide the optimization process. Different loss functions are suitable for different types of tasks:

• Regression: Mean squared error (MSE), mean absolute error (MAE)
• Classification: Cross-entropy loss, hinge loss
• Ranking: Hinge loss, pairwise ranking loss

Loss functions are minimized during training to improve the model’s accuracy.

Related Terms:

• Machine Learning
• Optimization
• Error
• Cost Function
• Objective Function

FAQs:

1. What is the difference between MSE and MAE?

   • MSE squares the errors, making it more sensitive to large errors. MAE takes the absolute value of the errors, making it less sensitive to outliers.

2. How is the loss function used in training a machine learning model?

   • The loss function is minimized using optimization algorithms like gradient descent to adjust the model’s parameters.

3. What is the role of the loss function in evaluating model performance?

   • The loss function provides a quantitative measure of the model’s accuracy on the training and validation sets.