In machine learning (ML), bias is a concept which is related to errors in the model's predictions, as a results of multiple assumptions and simplifications in the machine learning algorithm. Due to these assumptions, the ML model becomes easy to explain (explainability) but it often misses to capture the complexity inside the training and testing data of any given dataset in the problem domain. If a model has high bias, then it fails to perform well with both training and testing data and falls under the underfitting area outside the good fit.

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