Most introductions to regression metrics start with familiar names like MAE, MSE, and RMSE. However, serious models require a deeper understanding of these metrics and their implications.
This piece explores various regression losses, including R², MAPE, RMSLE, and Huber Loss, emphasizing the importance of choosing the right loss function to enhance model accuracy.
By understanding these advanced metrics, data scientists can make informed decisions that lead to better model performance and more reliable predictions.