Any supervised learning model is the result of optimizing the errors due to model complexity and the training error(prediction error on examples during training). Example: Ridge Regression or Regularized Linear Regression cost function(with parameters ) is given by is the mean squared error of the prediction made(by the model with parameters ) on training…

## How does bias and variance error gets introduced ?

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