Bias-variance tradeoff

The bias-variance tradeoff is a fundamental concept in machine learning that describes the problem of simultaneously minimizing two sources of error that prevent supervised learning algorithms from generalizing beyond their training set. Bias refers to the error due to overly simplistic assumptions in the learning algorithm. Variance refers to the error due to excessive complexity in the learning algorithm that leads to model sensitivity to high degrees of variation in the training data. Ideally, one aims to find a balance between bias and variance to minimize the total error.