Loss Function

A loss function, also known as a cost function or objective function, is a crucial component in machine learning that quantifies the error or "loss" associated with a model's predictions. It measures the discrepancy between the predicted output of the model and the actual ground truth, serving as a guiding mechanism for the algorithm during its training phase. The goal of any machine learning model is to minimize this loss, indicating that its predictions are becoming more accurate and aligned with the observed data.