compareData

Example multiclass classification dataset with the following wetland-related classes:
"PFO", "PEM", "RLP", and "Not". PFO = Palustrine Forested; PEM = Palustrine Emergent;
RLP = River, Lake, Pond; Not = Not Wetland. There are 600 examples from each class relative to the
reference labels.

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

compareData function

<dl>
<dt>ref</dt>
<dd>correct label</dd><dt>rfRred</dt>
<dd>random forest prediction</dd><dt>dfRred</dt>
<dd>single decision tree prediction</dd>
</dl>

Format

Data for multiclass classification comparison — compareData

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

<dt>rfRred</dt>
<dd>random forest prediction</dd>

<dt>dfRred</dt>
<dd>single decision tree prediction</dd>


</dl>

compareData: Data for multiclass classification comparison

Description

Arguments

Format

References