In machine learning, a loss function, also known as a cost function or objective function, quantifies the difference between the predicted values of a model and the true values (labels or targets) for a set of input data. The goal of training a machine learning model is to minimize this loss function, thereby improving the model’s predictive performance. Here’s a comprehensive overview of loss functions:
1. Purpose of Loss Functions:
i. Quantifying Error:
- Loss functions measure the difference between predicted values and actual values. A lower loss indicates better model performance.
ii. Optimization:
- During training, machine learning models adjust their parameters to minimize the loss function, optimizing the model for better predictions.
2. Types of Loss Functions:
i. Regression Loss Functions:
- Mean Squared Error (MSE): Common for regression tasks, penalizes large errors quadratically.
- Mean Absolute Error (MAE): Measures the average absolute difference between predicted and actual values.
- Huber Loss: A combination of MSE and MAE, less…
