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 😊

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.

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 😊

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 😊