Loss Functions

Loss functions play a pivotal role in the training of machine learning models. They are mathematical functions that quantify the difference between the predicted values by the model and the actual values in the training data. This difference is commonly known as "loss" or "error." The primary objective of a machine learning algorithm during the training phase is to minimize this loss, which essentially means improving the accuracy of predictions made by the model.

Common loss functions include Mean Square Error (MSE), which calculates the mean squared difference between predictions and actuals. Ther is also Mean Absolute Error (MAE), which calculates the mean absolute difference. Huber Loss combines MSE and MAE to provide a balanced approach. Log Loss, or cross-entropy loss, is used in classification tasks to measure the dissimilarity between predicted and true class probabilities. Hinge Loss, used in binary classification, promotes correct classification with a margin of error.

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