The article explores various loss functions used in machine learning for regression models, emphasizing their roles in minimizing prediction errors. It differentiates between Mean Square Error (MSE) and Mean Absolute Error (MAE), highlighting that MSE is sensitive to outliers while MAE is more robust against them. The piece also introduces Huber loss as a balance between MSE and MAE, providing robustness to outliers with a tunable hyperparameter, and Log-cosh loss, which combines the benefits of MSE and MAE while being twice differentiable, making it favorable for certain machine learning frameworks. Quantile loss is discussed for its utility in providing prediction intervals, especially in heteroscedastic data scenarios, offering a more flexible approach compared to traditional regression methods. The article suggests that choosing the appropriate loss function depends on the data characteristics and the specific needs of the model, such as outlier sensitivity and prediction accuracy.