The * metrica* package was developed to
visualize and compute the level of agreement between observed
ground-truth values and model-derived (e.g., mechanistic or empirical)
predicted.

This package is intended to fit into the following workflow:

- a data set containing the observed values is used to train a
model

- the trained model is used to generate predicted

- a data frame containing at least the
**observed**and model-**predicted**values is created

package is used to compute and evaluate the classification model based on observed and predicted values`metrica`

package is used to visualize model fit and selected fit metrics`metrica`

This vignette introduces the functionality of the
* metrica* package applied to observed and
model-predicted values of a binary land cover classification scenario,
where the two classes are vegetation (1) and non-vegetation (0)).

Letâ€™s begin by loading the packages needed.

## Libraries

```
library(metrica)
library(dplyr)
library(purrr)
library(tidyr)
```

Now we load the binary `land_cover`

data set already
included with the `metrica`

package. This data set contains
two columns:

`predicted`

: model-predicted (random forest) land cover, being vegetation = 1 and other = 0,`actual`

: ground-truth observed land cover, being 0 = vegetation and 1 = other

```
# Load
<- metrica::land_cover
binary_landCover
# Printing first observations
head(binary_landCover)
#> actual predicted
#> 1 0 0
#> 2 1 1
#> 3 1 1
#> 4 0 0
#> 5 0 0
#> 6 1 1
```