Loss functions are a fundamental component in machine learning, serving as a measure of how well a predictive model performs. They quantify the difference between the predicted values generated by the model and the actual values from the dataset. By minimizing the loss function during training, the model learns to make more accurate predictions.

There are various types of loss functions tailored to different machine learning tasks. For regression tasks, common loss functions include Mean Squared Error (MSE) and Mean Absolute Error (MAE). MSE emphasizes larger errors due to squaring the differences, while MAE treats all errors equally. In classification tasks, loss functions like cross-entropy are often used to evaluate how well the model distinguishes between different classes ³⁴.

The choice of loss function can significantly impact the model’s performance and its ability to generalize to new data. Additionally, loss functions are crucial for training neural networks, as they help compute gradients for backpropagation, which updates the model’s weights ⁴.