Machine learning models can be parametric or non-parametric. Parametric models are those that require the specification of some parameters before they can be used to make predictions, while non-parametric models do not rely on any specific parameter settings and therefore often produce more accurate results. This session will discusses parametric vs non-parametric machine learning models with examples along with the key differences.

Training machine learning models is about finding a function approximation built using input or predictor variables, and whose output represents the response variable. The reason why it is called function approximation is because there is always an error in relation to the value of function output vs actual or real-world value. And, an aspect of this error is reducible in the sense that further features / technique can be used to improve upon the accuracy. Another aspect of this error is irreducible which represents the random error which canâ€™t be dealt with.

The following is the list of differences between parametric and non-parametric machine learning models.

- In case of parametric models, the assumption related to the functional form is made and linear model is considered. In case of non-parametric models, the assumption about the functional form is not made.
- Parametric models are much easier to fit than non-parametric models because parametric machine learning models only require the estimation of a set of parameters as the model is identified prior as linear model. In case of non-parametric model, one needs to estimate some arbitrary function which is a much difficult task.
- Parametric models often do not match the unknown function we are trying to estimate. The model performance is comparatively lower than the non-parametric models. The estimates done by the parametric models will be farther from being true.
- Parametric models are interpretable unlike the non-parametric models. at one can go for parametric models when the goal is to find inference. Instead, one can choose to go for non-parametric models when the goal is to make prediction with higher accuracy and interpretability or inference is not the key ask.