wconf: Weighted Confusion Matrix
Allows users to create weighted confusion matrices and accuracy
metrics that help with the model selection process for classification
problems, where distance from the correct category is important. The
package includes several weighting schemes which can be parameterized, as
well as custom configuration options. Furthermore, users can decide
whether they wish to positively or negatively affect the accuracy score
as a result of applying weights to the confusion matrix. Functions are
included to calculate accuracy metrics for imbalanced data. Finally,
'wconf' integrates well with the 'caret' package, but it can also work
standalone when provided data in matrix form.
References:
Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package"
<doi:10.18637/jss.v028.i05>
Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization
(and the mewto R package)" <doi:10.2139/ssrn.3805911>
Monahov, A. (2024) "Improved Accuracy Metrics for Classification with
Imbalanced Data and Where Distance from the Truth Matters, with the wconf R
Package" <doi:10.2139/ssrn.4802336>
Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced
Data Classification Results. Pattern Recognition and Image Analysis, 295–302
Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023)
"A general framework for implementing distances for categorical variables"
<doi:10.48550/arXiv.2301.02190>.
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