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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

mse function

<dl><dt>trues</dt>
<dd>Target values from a test set of a given data set. Should be a vector and have the same size as the variable preds</dd>
<dt>preds</dt>
<dd>Predicted values given a certain test set of a given data set. Should be a vector and have the same size as the variable trues</dd></dl>

Arguments

Mean Squared Error — mse

<dl>

<dt>trues</dt>
<dd>Target values from a test set of a given data set. Should be a vector and have the same size as the variable preds</dd>


<dt>preds</dt>
<dd>Predicted values given a certain test set of a given data set. Should be a vector and have the same size as the variable trues</dd>

</dl>

mse: Mean Squared Error

Description

Usage

Value

Arguments