Bias-Variance Tradeoff
In the world of machine learning when discussing model prediction, it’s important to understand prediction errors (bias and variance). The prediction error for any machine learning algorithm can be broken down into three parts:
Bias Error Variance Error Irreducible Error Bias Bias is the difference between the average prediction of a model and the correct true value which is being predicted. A model with high bias does not perform well on both the training and testing data due to oversimplification.