The phi function retrieves the relevance value of the values in a target variable. It does so by resorting to the Piecewise Cubic Hermite Interpolation Polynomial method for interpolating over a set of maximum and minimum relevance points. The notion of relevance is associated with rarity.Nonetheless, this notion may depend on the domain experts knowledge

Imbalanced domain learning has almost exclusively focused on solving
classification tasks, where the objective is to predict cases labelled with a
rare class accurately. Such a well-defined approach for regression tasks lacked
due to two main factors. First, standard regression tasks assume that each value
is equally important to the user. Second, standard evaluation metrics focus on
assessing the performance of the model on the most common cases. This package
contains methods to tackle imbalanced domain learning problems in regression
tasks, where the objective is to predict extreme (rare) values.
The methods contained in this package are: 1) an automatic and non-parametric
method to obtain such relevance functions; 2) visualisation tools; 3) suite of
evaluation measures for optimisation/validation processes; 4) the squared-error
relevance area measure, an evaluation metric tailored for imbalanced regression tasks.
More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.

Nuno Moniz

IRon

Solving Imbalanced Regression Tasks

Rita P. Ribeiro

Miguel Margarido

phi function

<dl><dt>y</dt>
<dd>The target variable of a given data set</dd>
<dt>phi.parms</dt>
<dd>The relevance function providing the data points where the pairs of values-relevance are known</dd></dl>

Arguments

Obtain the relevance of data points — phi

<dl>

<dt>y</dt>
<dd>The target variable of a given data set</dd>


<dt>phi.parms</dt>
<dd>The relevance function providing the data points where the pairs of values-relevance are known</dd>

</dl>

phi: Obtain the relevance of data points

Description

Usage

Value

Arguments

Examples