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