About this document
Bias vs Variance in Machine Learning by vishesh.yo.34 is a document available to read on EtoBox.
The document discusses the concepts of bias and variance in machine learning, explaining that bias refers to errors due to oversimplification leading to underfitting, while variance refers to errors due to excessive complexity leading to overfitting. It emphasizes the trade-off between bias and variance, noting that increasing one typically decreases the other, and outlines methods to combat overfitting and underfitting. The ultimate goal is to achieve a model with low bias and low variance for optimal pred
- Author
- vishesh.yo.34
- Language
- EN