Core of this package is a heuristic optimization procedure (Simulated Annealing) that allows for identifying optimal classification schemes for prediction models with continuous response variables. The implemented methods were primarily developed to quantify the classification accuracy of prediction maps based on statistical models that provide predictions on a continuous scale. In many cases, these continuous predictions are afterwards discretized into classes for better visualization purposes without considering the resulting accuracies of the classification scheme. In a more general context, the optimization method can also be used to detect non-constant prediction performance of statistical models.
|Maintainer||Andreas Hill <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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