land_cover

This example dataset was obtained in 2022 over a small region in Kansas,
using visual interpretation.
The predicted values were the result of a Random Forest classification,
with a 70/30 % split.
Values equal to 1 are associated to vegetation, and values equal to 0 are other type of land cover.

datasets

A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.

Adrian A. Correndo

metrica

Prediction Performance Metrics

Luiz H. Moro Rosso

Rai Schwalbert

Carlos Hernandez

Leonardo M. Bastos

Luciana Nieto

Dean Holzworth

Ignacio A. Ciampitti

land_cover function

This data frame has 284 rows and the following 2 columns:<dl>
<dt>predicted</dt>
<dd>predicted values</dd><dt>actual</dt>
<dd>observed values</dd>
</dl>

Format

Binary Land Cover Data — land_cover

This data frame has 284 rows and the following 2 columns:<dl>
<dt>predicted</dt>
<dd>predicted values</dd>

<dt>actual</dt>
<dd>observed values</dd>


</dl>

Binary Land Cover Data

land_cover: Binary Land Cover Data

Description

Usage

Arguments

Format