air_quality

The data set consists of hourly averaged measurements from an array
of 5 metal oxide chemical sensors embedded in an air quality
chemical multisensor device. The outcome variable <code>y</code> is the
corresponding true hourly averaged concentration CO taken from a
reference analyzer. The input variables <code>x</code> are taken to be the
corresponding hourly averaged measurements of the 13 other
quantities.

datasets

Contrast trees represent a new approach for assessing the
accuracy of many types of machine learning estimates that are not
amenable to standard (cross) validation methods; see "Contrast
trees and distribution boosting", Jerome H. Friedman (2020)
<doi:10.1073/pnas.1921562117>. In situations where inaccuracies
are detected, boosted contrast trees can often improve
performance. Functions are provided to to build such trees in
addition to a special case, distribution boosting, an assumption
free method for estimating the full probability distribution of an
outcome variable given any set of joint input predictor variable
values.

Balasubramanian Narasimhan

conTree

Contrast Trees and Boosting

Jerome Friedman

air_quality function

<div class="section" id="air-quality">
<h3><code>air_quality</code></h3>
A list with 4 items.<dl>
<dt>xco</dt>
<dd>data frame of 9357 observations on 13 variables</dd><dt>yco</dt>
<dd>hourly averaged CO concentration</dd><dt>zco</dt>
<dd>sample membership indicator</dd><dt>pr2</dt>
<dd>probability propensity score</dd>
</dl></div>

Format

Air Quality Data from UC Irvine Machine Learning Repository — air_quality

<div class='section' id='air-quality'>
<h3><code>air_quality</code></h3>


A list with 4 items.<dl>
<dt>xco</dt>
<dd>data frame of 9357 observations on 13 variables</dd>

<dt>yco</dt>
<dd>hourly averaged CO concentration</dd>

<dt>zco</dt>
<dd>sample membership indicator</dd>

<dt>pr2</dt>
<dd>probability propensity score</dd>


</dl>

</div>

air_quality: Air Quality Data from UC Irvine Machine Learning Repository

Description

Usage

Arguments

Format

`air_quality`