bias-variance-tradeoff

The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two types of errors: bias, which refers to the error due to overly simplistic assumptions in the learning algorithm, and variance, which refers to error due to excessive sensitivity to fluctuations in the training set. A good model strikes a balance between bias and variance, enabling it to generalize well to unseen data while maintaining accuracy.