Generalization and evaluation
Once the model is built, how do we know it will perform on new data? Is this model any good? To answer these questions, we'll first look into the model generalization and then, see how to get an estimate of the model performance on new data.
Underfitting and overfitting
Predictor training can lead to models that are too complex or too simple. The model with low complexity (the leftmost models) can be as simple as predicting the most frequent or mean class value, while the model with high complexity (the rightmost models) can represent the training instances. Too rigid modes, which are shown on the left-hand side, cannot capture complex patterns; while too flexible models, shown on the right-hand side, fit to the noise ...