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IRon (version 0.1.4)

Solving Imbalanced Regression Tasks

Description

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) .

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Install

install.packages('IRon')

Version

0.1.4

License

CC0

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Last Published

January 20th, 2023

Functions in IRon (0.1.4)

ser

Non-Standard Evaluation Metrics
sera

Squared Error-Relevance Area (SERA)
rmse

Root Mean Squared Error
variance

Model Variance
phiPlot

Plot of phi versus y and boxplot of y
phi.range

Custom Relevance Function
eval.stats

Predictive Modelling Evaluation Statistics
corr

Pearson's Correlation
mae

Standard Evaluation Metrics
bias

Model Bias
phi.control

Generation of relevance function
phi

Obtain the relevance of data points
NO2Emissions

NO2Emissions
phi.extremes

Relevance function for extreme target values
mse

Mean Squared Error
accel

Acceleration