Loss Function Calculator
A loss function is a mathematical function used in machine learning and optimization to quantify how well a model’s predictions match the actual data. It measures the discrepancy between the predicted outputs of the model and the true target values, providing a way to assess the performance of the model. The goal of training a machine learning model is to minimize this loss, thereby improving the accuracy of the model’s predictions.
There are various types of loss functions depending on the type of task:
- Mean Squared Error (MSE): Commonly used for regression tasks, it calculates the average squared difference between predicted and actual values.
- Cross-Entropy Loss (Log Loss): Used for classification tasks, particularly in binary and multi-class classification, it measures the difference between the predicted probability distribution and the actual distribution.
- Hinge Loss: Used for support vector machines, it helps separate classes by maximizing the margin between them.
- Binary Cross-Entropy Loss: Specifically for binary classification problems, comparing the predicted probability with the actual binary label.
The choice of loss function depends on the type of problem you’re solving (e.g., regression, classification).