Developer Guide

Contents

Quality Metrics for Linear Regression

Given a data set
X
= (
x
i
) that contains vectors of input variables
x
i
= (
x
i
1
, …,
x
ip
), respective responses
z
i
= (
z
i
1
, …,
z
ik
) computed at the prediction stage of the linear regression model defined by its coefficients
β
ht
,
h
= 1, ...,
k
,
t
= 1, ...,
p
, and expected responses
y
i
= (
y
i
1
, …,
y
ik
),
i
= 1, ...,
n
, the problem is to evaluate the linear regression model by computing the root mean square error, variance-covariance matrix of beta coefficients, various statistics functions, and so on. See Linear Regression for additional details and notations.
For linear regressions, the library computes statistics listed in tables below for testing insignificance of beta coefficients and one of the following values of
QualityMetricsId
:
For more details, see [Hastie2009].

Product and Performance Information

1

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