Polynomial Regression

Polynomial Regression

Polynomial Regression

Description

Polynomial Regression is a supervised learning method in which the relationship between the independent and dependent variables is modeled as an nth degree polynomial.

Why to use

Predictive Modeling

When to use

When the data points are not captured by the Linear Regression Model and the Linear Regression fails in describing the best result clearly.

When not to use

On Textual data.

Prerequisites

  • If the data contains any missing values, use Missing Value Imputation before proceeding with Polynomial Regression.
  • If the input variable is of categorical type, use Label Encoder.
  • The output variable must be a continuous data type.
  • Linearity – The relationship between the dependent and independent variables should be linear.
  • Independence – The variables should be independent of each other.
  • Normality – The variables should be normally distributed.
  • The Dependent variable (Y) vs. Residuals plot must not follow a pattern.
  • The errors should be normally distributed.

Input

Any continuous data

Output

The predicted value of the dependent variables.

Statistical Methods used

  • Fit Intercept
  • Dimensionality Reduction

Limitations

It cannot be used on textual data.


Polynomial Regression is a specific case of Linear Regression. In Polynomial Regression, a polynomial equation is fitted between the dependent and independent variables. It establishes a curvilinear relationship between the dependent variable and independent variables. In a curvilinear relationship, the value of the dependent variable changes in a non-uniform manner with respect to the independent variables.

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