loss functions

Loss functions are tools used in machine learning to measure how well a model's predictions match the actual outcomes. Think of it as a scorecard: the smaller the loss, the better the model's predictions are aligned with real data. During training, the model adjusts itself to minimize this loss, improving its accuracy over time. Different loss functions suit different types of tasks—such as predicting numbers or classifying categories—by quantifying errors in a way that guides the model toward better performance.