Provides model explainability using DALEX. This function:
Creates a DALEX explainer.
Computes permutation-based variable importance with boxplots showing variability, displays the table and plot.
Computes partial dependence-like model profiles if `features` are provided.
Computes Shapley values (SHAP) for a sample of the training observations, displays the SHAP table, and plots a summary bar chart of \(\text{mean}(\vert \text{SHAP value} \vert)\) per feature. For classification, it shows separate bars for each class.
fastexplain(
object,
method = "dalex",
features = NULL,
grid_size = 20,
shap_sample = 5,
vi_iterations = 10,
seed = 123,
loss_function = NULL,
...
)Prints DALEX explanations: variable importance table & plot, model profiles (if any), and SHAP table & summary plot.
A fastml_model object.
Currently only "dalex" is supported.
Character vector of feature names for partial dependence (model profiles). Default NULL.
Number of grid points for partial dependence. Default 20.
Integer number of observations from processed training data to compute SHAP values for. Default 5.
Integer. Number of permutations for variable importance (B). Default 10.
Integer. A value specifying the random seed.
Function. The loss function for model_parts.
If NULL and task = 'classification', defaults to DALEX::loss_cross_entropy.
If NULL and task = 'regression', defaults to DALEX::loss_root_mean_square.
Additional arguments (not currently used).
Custom number of permutations for VI (vi_iterations):
You can now specify how many permutations (B) to use for permutation-based variable importance. More permutations yield more stable estimates but take longer.
Better error messages and checks:
Improved checks and messages if certain packages or conditions are not met.
Loss Function:
A loss_function argument has been added to let you pick a different performance measure (e.g., loss_cross_entropy for classification, loss_root_mean_square for regression).
Parallelization Suggestion: