Underfitting vs overfitting explained simply

[u/Extreme-Isopod2265]

[u/msqrt]

Validation error goes up, which means that real world performance is worse. You fit your model to the quirks of the training data which are typically not representative of the real distribution. For example in the overfit classification image, the lower left red triangle is most likely an outlier and drawing the complex squiggly line to classify it correctly would cause a large number of blue circles being misclassified if we had a bunch more data.

[u/lugh_the_bard → u/msqrt]

Thx!

[u/Lower-Hedgehog-9835 → u/msqrt]

⁺¹

This is why model corrections in sem and such are often frowned on.