`R/get_evaluation.R`

`get_evaluation.Rd`

Returns performance metrics produced by `rf_evaluate()`

.

get_evaluation(model)

model | A model fitted with |
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A data frame with evaluation scores. The following columns are shown:

`model`

: Identifies the given model. The values are "Full", (original model introduced into`rf_evaluate()`

), "Training" (model trained on an independent training spatial fold), and "Testing" (predictive performance of the training model on an independent testing spatial fold). The performance values of the "Testing" model represent the model performance on unseen data, and hence its ability to generalize.`metric`

: Four values representing different evaluation metrics, "rmse", "nrmse", "r.squared", and "pseudo.r.squared".`mean`

,`sd`

,`min`

, and`max`

: Average, standard deviation, minimum, and maximum of each metric across the evaluation (cross-validation) iterations.

if(interactive()){ #loading data data(plant_richness_df) data(distance_matrix) #fitting a random forest model rf.model <- rf( data = plant_richness_df, dependent.variable.name = "richness_species_vascular", predictor.variable.names = colnames(plant_richness_df)[5:21], distance.matrix = distance_matrix, distance.thresholds = 0, n.cores = 1, verbose = FALSE ) #evaluating the model with spatial cross-validation rf.model <- rf_evaluate( model = rf.model, xy = plant_richness_df[, c("x", "y")], n.cores = 1, verbose = FALSE ) #getting evaluation results from the model x <- get_evaluation(rf.model) x }