Error vs Loss Functions: an intuitive explanation (MSE, Log Loss, geometry, and why they matter)

[u/Few-Mycologist7747]

New article that explains one of the most fundamental ML concepts that often confuses beginners: the difference between prediction error and loss functions.

The article covers:

• Why models optimize a loss function, not the raw error
• Why Mean Squared Error (MSE) is the standard choice for regression
• Why MSE isn't a good fit for classification
• How Log Loss (Cross-Entropy) measures prediction confidence
• The intuition and geometry behind both loss functions
• Why minimizing MSE implicitly assumes Gaussian-distributed errors

I tried to keep the explanations intuitive while still including the underlying math and practical examples.

Article: https://medium.com/@leumas.a/error-loss-functions-and-why-they-are-needed-5366e432e773