computes the mean squared error (MSE) for evaluating the accuracy of
ordered/unordered probability predictions

internal

An implementation of the Ordered Forest estimator as developed
in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly
estimates the conditional probabilities of models with ordered categorical
outcomes (so-called ordered choice models). Additionally to common machine
learning algorithms the 'orf' package provides functions for estimating
marginal effects as well as statistical inference thereof and thus provides
similar output as in standard econometric models for ordered choice. The
core forest algorithm relies on the fast C++ forest implementation from
the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.

Gabriel Okasa

Ordered Random Forests

mse function

<dl><dt>predictions</dt>
<dd>matrix of predictions (n x categories)</dd>
<dt>observed</dt>
<dd>vector of observed ordered categorical outcomes (n x 1)</dd></dl>

Arguments

Mean Squared Error — mse

<dl>

<dt>predictions</dt>
<dd>matrix of predictions (n x categories)</dd>


<dt>observed</dt>
<dd>vector of observed ordered categorical outcomes (n x 1)</dd>

</dl>

mse: Mean Squared Error

Description

Usage

Value

Arguments