KDnuggets Breaks Down 3 Machine Learning Loss Functions for Beginners
Updated
Updated · KDnuggets · Jun 19
KDnuggets Breaks Down 3 Machine Learning Loss Functions for Beginners
2 articles · Updated · KDnuggets · Jun 19
Summary
KDnuggets published a beginner guide framing a loss function as the numeric feedback that tells a model how wrong its prediction was during training.
3 common losses anchor the explainer: mean squared error for numeric prediction, mean absolute error for outlier-tolerant regression, and cross-entropy for classification probabilities.
The article contrasts loss with accuracy, arguing that two models can post the same accuracy yet differ in loss because confidence and mistake size still matter.
A step-by-step training loop ties the concept together: predict, measure loss, update with an optimizer, and repeat until loss falls—while rising validation loss can signal overfitting.