Bias #

The difference between the data points and the fit line is bias.

Variance #

The estimation diference across different set is variability

Ideal model should have low bias and low variance. Simple and complex models are created to find the optimal bias and variance across test and training set.

Finding optimal trade off #

• Regularization
• Boosting
• Bagging