A Loss Function is a mathematical measure used in machine learning to determine how far a model’s prediction is from the correct result. It compares the output produced by the model with the expected value and assigns a numerical score representing the prediction error. During training, the model uses this feedback to adjust its parameters and gradually improve its predictions.
Different tasks use different loss functions. For example, Mean Squared Error is commonly used for predicting numerical values, while Cross-Entropy Loss is often used for classification problems. A lower loss generally indicates that the model’s predictions are closer to the desired outcomes. Loss Functions are therefore an essential part of model training, optimization, and performance improvement.
