Regression

API

Regressors.

This package includes regression algorithms, a.k.a. regressors.

A regressor aims to find relationships between input and output variables. After being fitted to a training dataset, the regression algorithms can predict output values of new input data.

A regression algorithm consists of identifying a function \(f: \mathbb{R}^{n_{\textrm{inputs}}} \to \mathbb{R}^{n_{\textrm{outputs}}}\). Given an input point \(x \in \mathbb{R}^{n_{\textrm{inputs}}}\), the predict method of the regression algorithm will return the output point \(y = f(x) \in \mathbb{R}^{n_{\textrm{outputs}}}\). See supervised for more information.

Wherever possible, the regression algorithms should also be able to compute the Jacobian matrix of the function it has learned to represent. Thus, given an input point \(x \in \mathbb{R}^{n_{\textrm{inputs}}}\), the Jacobian prediction method of the regression algorithm should return the matrix

\[\begin{split}J_f(x) = \frac{\partial f}{\partial x} = \begin{pmatrix} \frac{\partial f_1}{\partial x_1} & \cdots & \frac{\partial f_1} {\partial x_{n_{\textrm{inputs}}}}\\ \vdots & \ddots & \vdots\\ \frac{\partial f_{n_{\textrm{outputs}}}}{\partial x_1} & \cdots & \frac{\partial f_{n_{\textrm{outputs}}}} {\partial x_{n_{\textrm{inputs}}}} \end{pmatrix} \in \mathbb{R}^{n_{\textrm{outputs}}\times n_{\textrm{inputs}}}.\end{split}\]

Use the RegressorFactory to access all the available regressors or derive either the BaseRegressor class to add a new one.

Algorithms

See the regression algorithms.

Examples

See the examples about regression.