Parametric and Non-Parametric Models in Machine Learning - Sefik Ilkin Serengil

Machine learning algorithms are classified as two distinct groups: parametric and non-parametric. Herein, parametricness is related to pair of model complexity and the number of rows in the train set. We can classify algorithms as non-parametric when model becomes more complex if number of samples in the training set increases. Vice versa, a model would be parametric if model becomes stable when number of examples in the training set increases. If we increase the number of instances, then the decision tree that is going to be built becomes more complex.