parameter learning

Provides a suite of helper functions and a collection of datasets used in the book <https://uncovering-data-science.netlify.app>. It is designed to make data science techniques accessible to individuals with minimal coding experience. Inspired by an ancient Persian idiom, the package likens this learning process to "eating the liver of data science," symbolizing deep and immersive engagement with the field.

Abdolreza Mohammadi

liver

"Eating the Liver of Data Science"

Reza Mohammadi

Kevin Burke

mae function

<dl> <dt>pred</dt>
<dd>a numerical vector of estimated values.</dd> <dt>actual</dt>
<dd>a numerical vector of actual values.</dd> <dt>weight</dt>
<dd>a numerical vector of weights the same length as <code>pred</code>.</dd> <dt>na.rm</dt>
<dd>a logical value indicating whether NA values in <code>pred</code> should be stripped before the computation proceeds.</dd></dl>

Arguments

Reza Mohammadi <a href="/link/a.mohammadi%40uva.nl?package=liver&version=1.19" data-mini-rdoc="liver::a.mohammadi@uva.nl">a.mohammadi@uva.nl</a> and Kevin Burke <a href="/link/kevin.burke%40ul.ie?package=liver&version=1.19" data-mini-rdoc="liver::kevin.burke@ul.ie">kevin.burke@ul.ie</a>

Author

Mean Absolute Error (MAE) — mae

<dl>

 <dt>pred</dt>
<dd>a numerical vector of estimated values.</dd>

 <dt>actual</dt>
<dd>a numerical vector of actual values.</dd>

 <dt>weight</dt>
<dd>a numerical vector of weights the same length as <code>pred</code>.</dd>

 <dt>na.rm</dt>
<dd>a logical value indicating whether NA values in <code>pred</code> should be stripped before the computation proceeds.</dd>

</dl>

Reza Mohammadi <a href='mailto:a.mohammadi@uva.nl'>a.mohammadi@uva.nl</a> and Kevin Burke <a href='mailto:kevin.burke@ul.ie'>kevin.burke@ul.ie</a>

mae: Mean Absolute Error (MAE)

Description

Usage

Value

Arguments

Author

See Also

Examples