 Loss functions for classification

In machine learning, particularly in the context of classification tasks, loss functions (or cost functions) are used to quantify how well the model's predictions match the actual labels of the data. These functions measure the discrepancy between the predicted output and the true output, guiding the optimization process during training. Here are some commonly used loss functions for classification problems: ### 1. **Binary Cross-Entropy Loss** - **Usage**: Used in binary classification problems.