Artificial Intelligence & Machine Learning
Loss Function
Definition
A loss function (or cost function) is a function that measures the "cost" or "error" of a model's predictions compared to the actual correct values. The goal of training a model is to find a set of parameters that minimizes this loss function.
Why It Matters
The loss function defines the objective of the machine learning model. It gives the model a clear signal of how well it is doing, which is then used by the optimization algorithm (like gradient descent) to improve it.
Contextual Example
In a regression problem, a common loss function is Mean Squared Error (MSE), which calculates the average of the squared differences between the predicted and actual values.
Common Misunderstandings
- The choice of loss function depends on the type of problem you are solving (e.g., Cross-Entropy for classification, MSE for regression).
- The process of training is the process of minimizing the loss function.