Assessing Model Accuracy - Part 2

In my last post, I have explained about MSE, today I will explain the variance & bias trade-off, Precision recall trade-off while assessing the model accuracy. Variance refers to the amount by which the estimated output (f) would change if we estimated it (f) using a different training dataset. Since the training data is used to fit the statistical learning method, different training sets will result in different outputs (f). Ideally, the estimate should not vary much between training sets. Bias refers to the error that is introduced by approximating a complicated problem by a simpler model.