BinaryEvaluationMetrics

Overview

BinaryEvaluationMetrics is an R package that provides a collection of methods for evaluating binary classification models. It includes novel metrics developed as part of my dissertation, offering alternatives to traditional accuracy measures.

Installation

You can install the package directly from GitHub using:

# Install devtools if you haven't already
install.packages("devtools")

# Install BinaryEvaluationMetrics
devtools::install_github("JulenPerales/BinaryEvaluationMetrics")

Features

• Clayton Skill Score (CSS): An alternative to commonly used skill scores for binary classification.
• More metrics to be added...

Usage

Load the package and use the available functions:

library(BinaryEvaluationMetrics)

# Example usage of Clayton Skill Score
actuals <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 0)
predictions <- c(0.9, 0.7, 0.8, 0.65, 0.3, 0.2, 0.9, 0.4, 0.1, 0)

OA <- Classifier(predictions,actuals)
plotClassifier(OA)
CSI <- Classifier(predictions,actuals,metric="CSI")
plotClassifier(CSI)

#Example using spatial data (Spatraster)

nrows <- 5
ncols <- 5

# Create an index raster with structured values (not random)
predictions <- rast(nrows=nrows, ncols=ncols)
values(predictions) <- seq(0.05, 1, length.out=ncell(index))

# Create a boolean raster ensuring 30% prevalence (30% of cells = 1)
actuals_values <- c(
  0, 0, 0, 0, 0,
  0, 1, 0, 1, 0,
  0, 0, 0, 1, 0,
  0, 1, 0, 0, 0,
  1, 0, 0, 0, 0
)
actuals <- rast(nrows=nrows, ncols=ncols, ext=ext)
values(actuals) <- actuals_values
OA <- Classifier(predictions,actuals)
plotClassifier(OA)
CSI <- Classifier(predictions,actuals,metric="CSI")
plotClassifier(CSI)

#Example for a single-threshold classification
predictions <- c(1, 1, 1, 1, 0, 0, 1, 0, 0, 0)
actuals <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 0)

Classification(predictions,actuals)

#Example for a single-threshold classification with maps
predictions_values <- c(
  1, 0, 0, 0, 0,
  0, 1, 0, 1, 0,
  0, 0, 0, 1, 0,
  1, 0, 1, 0, 0,
  0, 0, 0, 0, 1
)
predictions <- rast(nrows=nrows, ncols=ncols, ext=ext)
values(predictions) <- predictions_values
actuals <- rast(nrows=nrows, ncols=ncols, ext=ext)
values(actuals) <- actuals_values

Classification(predictions,actuals)

Contributing

If you’d like to contribute, feel free to fork the repository and submit pull requests!

License

This package is licensed under the MIT License.