Bias vs Variance

If a model performs poorly on the trained data, we say that the model underfits the data. If a model performs well on the training data, but poorly on the test data, the model overfits the data Simple models (e.g. Linear) tend to have high bias - This leads to underfitting (poor performance on training data). Too complex (or overtrained) models tend to have low bias but high variance - This leads to overfitting (poor performance on test data).