In a regression problem with 1 output variable and p input variables, we carry out a a principal components regression with p principal components constructed from the input variables. This will result in a significant level of dimensionality reduction. PCR is one of the leading methods for dimension reduction. no dimensionality reduction whatsoever. a potentially good level of dimensionality reduction depending on the quality of the principal components us making a bad choice if we care about the output variable. In that case we should have used partial least squares as a better high dimensionality reduction technique.

A type of statistical model is regression, it helps to understand the relationship between…

Answered: In a regression problem with 1 output…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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In a regression problem with 1 output variable and p input variables, we carry out a a principal components regression with p principal components constructed from the input variables. This will result in

		a significant level of dimensionality reduction. PCR is one of the leading methods for dimension reduction.
		no dimensionality reduction whatsoever.
		a potentially good level of dimensionality reduction depending on the quality of the principal components
		us making a bad choice if we care about the output variable. In that case we should have used partial least squares as a better high dimensionality reduction technique.