loss_score_ranking

<code>loss_score_ranking()</code> ranks tuning parameters according to the scores
received in multiple loss functions.

A tool that improves the prediction performance of multilevel
regression with post-stratification (MrP) by combining a number of machine
learning methods. For information on the method, please refer to Broniecki,
Wüest, Leemann (2020) ''Improving Multilevel Regression with
Post-Stratification Through Machine Learning (autoMrP)'' in the
'Journal of Politics'. Final pre-print version:
<https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.

Philipp Broniecki

autoMrP

Improving MrP with Ensemble Learning

Reto Wüest

Lucas Leemann

Florian Schaffner

Hadley Wickham

loss_score_ranking function

<dl><dt>score</dt>
<dd>A data set containing loss function names, the loss function
values, and the tuning parameter values.</dd>
<dt>loss.fun</dt>
<dd>Loss function. A character-valued scalar indicating whether
prediction loss should be measured by the mean squared error (<code>MSE</code>)
or the mean absolute error (<code>MAE</code>). Default is <code>MSE</code>.</dd></dl>

Arguments

Ranks tuning parameters according to loss functions — loss_score_ranking

<dl>

<dt>score</dt>
<dd>A data set containing loss function names, the loss function
values, and the tuning parameter values.</dd>


<dt>loss.fun</dt>
<dd>Loss function. A character-valued scalar indicating whether
prediction loss should be measured by the mean squared error (<code>MSE</code>)
or the mean absolute error (<code>MAE</code>). Default is <code>MSE</code>.</dd>

</dl>

loss_score_ranking: Ranks tuning parameters according to loss functions

Description

Usage

Value

Arguments