Gets performance data frame from a cross-validated model

Returns performance metrics produced by rf_evaluate().

get_evaluation(model)

Arguments

model

A model fitted with rf_evaluate().

Value

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.

See also

rf_evaluate(), plot_evaluation(), print_evaluation()

Examples

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

}