Loss Functions

What are Loss Functions?

Loss functions, also known as cost functions or objective functions, are used in machine learning to quantify the difference between the predicted values and the actual values of the target variable. The goal of a machine learning model is to minimize the loss function, which represents the error in the model’s predictions.

Common types of loss functions:

  1. Mean Squared Error (MSE) - used for regression tasks, calculates the average of the squared differences between actual and predicted values.
  2. Cross-Entropy Loss - used for classification tasks, measures the difference between two probability distributions (the actual class probabilities and the predicted class probabilities).
  3. Hinge Loss - used for Support Vector Machines, measures the error for classification tasks based on the margin between data points and the decision boundary.
  4. Huber Loss - a combination of MSE and Mean Absolute Error (MAE), used for regression tasks when there may be outliers in the data.

Resources on Loss Functions: