2
$\begingroup$

A model can be classified as parametric or non-parametric. How are models classified as parametric and non-parametric models? What is the difference between the two approaches?

$\endgroup$
3
$\begingroup$

Parametric Methods

A parametric approach (Regression, Linear Support Vector Machines) has a fixed number of parameters and it makes a lot of assumptions about the data. This is because they are used for known data distributions. i.e, it makes a lot of presumptions about the data

Non-Parametric Methods

A non-parametric approach (k-Nearest Neighbours, Decision Trees) has a flexible number of parameters, there are no presumptions about the data distribution. The model tries to "explore" the distribution and thus has a flexible number of parameters.

Comparision

Comparatively speaking, parametric approaches are computationally faster and have more statistical power when compared to non-parametric methods


Hope this cleared your doubts 😊

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.