NO2Emissions

<p>The data are a subsample of 500 observations from a data set that originate in a study where air pollution at a road is
related to traffic volume and meteorological variables, collected by the Norwegian Public Roads Administration.
The response variable (column 1) consist of hourly values of the logarithm of the concentration of NO2 (particles),
measured at Alnabru in Oslo, Norway, between October 2001 and August 2003. The predictor variables (columns 2 to 8)
are the logarithm of the number of cars per hour, temperature $2$ meter above ground (degree C), wind speed
(meters/second), the temperature difference between $25$ and $2$ meters above ground (degree C),
wind direction (degrees between 0 and 360), hour of day and day number from October 1. 2001.</p>

datasets

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

NO2Emissions function

<p>A <code>"data.frame"</code> structure with 500 observations, 8 numerical variables</p>

NO2Emissions: NO2Emissions

Description

Usage

Arguments

Format

Examples