M_Breiman

This function evaluates the importance of each predictor by the notion of surrogate splits.

Functions are provided to determine production frontiers and technical
efficiency measures through non-parametric techniques based upon regression trees.
The package includes code for estimating radial input, output, directional and
additive measures, plotting graphical representations of the scores and the production
frontiers by means of trees, and determining rankings of importance of input variables
in the analysis. Additionally, an adaptation of Random Forest by a set of individual
Efficiency Analysis Trees for estimating technical efficiency is also included. More
details in: <doi:10.1016/j.eswa.2020.113783>.

Miriam Esteve

Efficiency Analysis Trees

Víctor España

Juan Aparicio

Xavier Barber

M_Breiman function

<dl><dt>object</dt>
<dd>An EAT object.</dd>
<dt>digits</dt>
<dd>Decimal units.</dd></dl>

Arguments

Breiman Importance — M_Breiman

<dl>

<dt>object</dt>
<dd>An EAT object.</dd>


<dt>digits</dt>
<dd>Decimal units.</dd>

</dl>

M_Breiman: Breiman Importance

Description

Usage

Value

Arguments