biData

Example binary classification dataset. "Mine" is the positive case and
"Not Mine" is the background class. There are 178 samples from the "Mine" class and
4,822 samples from the "Not Mine" class. Counts are relative to reference labels.
Class proportions are based on landscape proportions. There are a total of 5,000 samples.

datasets

Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021)<doi:10.1109/ACCESS.2021.3116526> and Tang et al. (2024)<doi:10.1109/TGRS.2024.3446950>). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results.

Aaron Maxwell

micer

Map Image Classification Efficacy

Sarah Farhadpour

biData function

<dl>
<dt>ref</dt>
<dd>reference label</dd><dt>pred</dt>
<dd>predicted label</dd>
</dl>

Format

Example binary classification dataset — biData

<dl>
<dt>ref</dt>
<dd>reference label</dd>

<dt>pred</dt>
<dd>predicted label</dd>


</dl>

biData: Example binary classification dataset

Description

Arguments

Format

References