\(\DeclarePairedDelimiterX{\Set}[2]{\{}{\}}{#1 \nonscript\;\delimsize\vert\nonscript\; #2}\) \( \DeclarePairedDelimiter{\set}{\{}{\}}\) \( \DeclarePairedDelimiter{\parens}{\left(}{\right)}\) \(\DeclarePairedDelimiterX{\innerproduct}[1]{\langle}{\rangle}{#1}\) \(\newcommand{\ip}[1]{\innerproduct{#1}}\) \(\newcommand{\bmat}[1]{\left[\hspace{2.0pt}\begin{matrix}#1\end{matrix}\hspace{2.0pt}\right]}\) \(\newcommand{\barray}[1]{\left[\hspace{2.0pt}\begin{matrix}#1\end{matrix}\hspace{2.0pt}\right]}\) \(\newcommand{\mat}[1]{\begin{matrix}#1\end{matrix}}\) \(\newcommand{\pmat}[1]{\begin{pmatrix}#1\end{pmatrix}}\) \(\newcommand{\mathword}[1]{\mathop{\textup{#1}}}\)
Needs:
Real Numbers
Predictors
Needed by:
Least Squares Linear Regressors
Neural Networks
Normal Linear Model Regressors
Links:
Sheet PDF
Graph PDF
Wikipedia

Regressors

Why

We name a predictor whose codomain (its set of outputs) is the real numbers.

Definition

A regressor is a predictor whose codomain is the set of real numbers. Following this terminology, but speaking of processes, some1 authors refer to the problem of constructing a regressor as regression or a regression problem.


  1. Most, nearly all, universally. We do not use this because Bourbaki deals with mathematical objects, for which there is no concept of time. ↩︎
Copyright © 2023 The Bourbaki Authors — All rights reserved — Version 13a6779cc About Show the old page view