ML bias is a phenomenon where some aspects of datasets with equal significance are given more weight or representation than others, leading to skewed outcomes. In such cases, the errors are magnified in the final analytical results rendering the ML model inappropriate and ineffective.

In its simplest terms, bias is the situation where the model consistently predicts distorted results because of incorrect assumptions. When we train our model on a training set and evaluate it on a training set, a biased model produces significant losses or errors.