In machine learning, a loss function (also known as a cost function or error function) is a mathematical construct that quantifies the difference between the predicted outputs of a model and the actual target values. This function assigns a real number to each prediction, representing the “cost” or error associated with that prediction. The primary objective during training is to minimize this loss, thereby enhancing the model’s accuracy and performance.
Key Aspects of Loss Functions:
- Purpose: Loss functions serve as a guide for optimization algorithms, indicating how well a model’s predictions align with the actual outcomes. A higher loss value signifies a greater discrepancy between predicted and actual values, while a lower loss indicates better performance.
- Types: There are various loss functions tailored to specific tasks:
- Mean Squared Error (MSE): Commonly used in regression tasks, MSE calculates the average of the squares of the differences between predicted and actual values.
- Cross-Entropy Loss: Predominant in classification problems, this loss measures the difference between two probability distributions—the predicted and the actual class distributions.
- Hinge Loss: Often employed in support vector machines, hinge loss is used for classification tasks, particularly when the goal is to maximize the margin between classes.
- Optimization: During training, optimization algorithms adjust the model’s parameters to minimize the loss function. This process involves computing the gradient of the loss with respect to the model’s parameters and updating them in the direction that reduces the loss.