How does bias and variance error gets introduced ?

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…