DALEX Explainer for h3sdm Workflows — h3sdm

Combines species presence/absence data with predictor variables and generates a DALEX explainer suitable for model diagnostics.

Usage

h3sdm_explain(model, data, response = "presence", label = "h3sdm workflow")

Arguments

model: A fitted workflow returned by h3sdm_fit_model().
data: A data.frame or sf object containing the original predictors and response variable. If an sf object, the geometry is dropped automatically.
response: Character string specifying the name of the response column. Must be a binary factor or numeric vector (0/1). Defaults to "presence".
label: Character string specifying a label for the explainer. Defaults to "h3sdm workflow".

Value

An object of class explainer from the DALEX package, ready to be used with feature_importance(), model_performance(), predict_parts(), and other DALEX functions.

Details

Create a DALEX explainer for h3sdm workflows

This function creates a DALEX explainer for a species distribution model fitted with h3sdm_fit_model(). It prepares the response and predictor variables, ensuring that all columns used during model training (including h3_address and coordinates) are included. The explainer can then be used for feature importance, model residuals, and other DALEX diagnostics.

Examples

# \donttest{
library(h3sdm)
library(DALEX)
#> Welcome to DALEX (version: 2.5.2).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
library(parsnip)

# Dataset de ejemplo con factor
dat <- data.frame(
  x1 = rnorm(20),
  x2 = rnorm(20),
  presence = factor(sample(0:1, 20, replace = TRUE))
)

# Modelo simple
f <- list(
  final_model = logistic_reg() |>
    fit(presence ~ x1 + x2, data = dat)
)

# Explainer
expl <- h3sdm_explain(f$final_model, data = dat, response = "presence")
#> Preparation of a new explainer is initiated
#>   -> model label       :  h3sdm workflow 
#>   -> data              :  20  rows  2  cols 
#>   -> target variable   :  20  values 
#>   -> predict function  :  custom_predict 
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package parsnip , ver. 1.3.3 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.04079885 , mean =  0.6 , max =  0.9990131  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.8074574 , mean =  5.720977e-15 , max =  0.705885  
#>   A new explainer has been created!  
feature_importance(expl)
#>       variable mean_dropout_loss          label
#> 1 _full_model_         0.1666667 h3sdm workflow
#> 2           x1         0.2333333 h3sdm workflow
#> 3           x2         0.5208333 h3sdm workflow
#> 4   _baseline_         0.5072917 h3sdm workflow
# }