Error analysis in supervised machine learning

Every supervised learning problem encounters either bias or variance error. Please refer to this page if you want to get more intuition about bias and variance error as it will help in understanding this post. Once you know where(bias or variance) your model is doing wrong, it becomes easier to get the next direction. This…

Whether to reduce bias error or variance error ?

Supervised learning model’s error can be decomposed in form of bias error and variance error. Read here to get the best intuition about bias and variance error.¬† When a model doesn’t perform well either on training data or testing data, either of bias error or variance error or both error might be the issue. Therefore…

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…