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Double Descent in Machine Learning Models by viktor.nezveda4 is a document available to read on EtoBox.

What is Double Descent in Machine Learning Models about?

This document discusses how modern machine learning practice appears to contradict the classical understanding of the bias-variance tradeoff. The classical view is that models should balance underfitting and overfitting, but modern neural networks are able to fit training data exactly without overfitting. The document proposes a "double descent" performance curve that reconciles classical and modern approaches by showing test performance can improve even as models fit training data more precisely. Evidence

Author

viktor.nezveda4

Language

EN