• December 23, 2024

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:

  1. Mean Squared Error (MSE): Commonly used for regression tasks, it calculates the average squared difference between predicted and actual values.
  2. 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.
  3. Hinge Loss: Used for support vector machines, it helps separate classes by maximizing the margin between them.
  4. 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).

Loss Function Calculator

Loss Function Calculator

Mean Squared Error

Cross-Entropy Loss

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