# Non-Parametric Regression vs Parametric Regression

## An Introduction

1. The outputs are called the responses, or classically the dependent variables.
2. A function that maps or explains the relation between input and output. in supervised learning, they are well known as regression functions. They correspond to the case where the outputs are quantitative.
1. If we do not have any model, we use a non-Parametric approach

## Why the non-parametric model?

1. It allows great flexibility in the possible form of the regression curve and makes no assumption about a parametric form.
2. It only assumes that the regression curve belongs to them some infinite-dimensional collection of functions.
3. It heavily relies on the experimenter to supply only qualitative information about the function and let the data speak for itself concerning the actual form of a regression curve.
4. It is best suited for inference in a situation where there is very little or no prior information about the regression curve.

## Conclusion

The parametric form of regression is used based on historical data; non-parametric can be used at any stage as it doesn’t take any presumption. However, parametric and non-parametric regression is not a contradiction of each other. While the subsequent computation of non-parametric form, based on the first result can be used as a parametric form, any type of parametric regression can be used as non-parametric to test the accuracy of the model that we are using for parametric regression.

Written by

Written by

## Suraj Ghimire

#### Follower of Jesus Christ | Data Science Student | I write stories of a random heart that I come across | Connect with me https://linktr.ee/authorsuraj 