Aggregate model importance scores across tasks to compute overall importance for each model

Aggregate model importance scores across tasks to compute overall importance for each model

Usage

# S3 method for class 'model_imp_tbl'
aggregate(
  x,
  by = "model_id",
  na_action = c("drop", "worst", "average"),
  fun = mean,
  ...
)

Arguments

x

An object of class model_imp_tbl.

by

A character vector with column names specifying the grouping variable(s) for summarization. Default is "model_id", which summarizes importance scores for each model across all tasks.

na_action

A character string specifying how to handle NA values generated during importance score calculation for each task, occurring when a model did not contribute to the ensemble prediction for a given task by missing its forecast submission. Three options are available: c("drop", "worst", "average"). For each specific prediction task, each option works as follows:

• "drop" removes NAs.

• "worst" replaces NAs with the smallest value among importance metrics of the other models.

• "average" replaces NAs with the average value from the other models' importance metrics.

fun

A function used to summarize importance scores. Default is mean()

...

Additional arguments passed to the summary function fun. (e.g., fun = quantile, probs = 0.25 for a quartile summary)

Value

A data frame with columns model_id and importance_score_<fun>, where <fun> is the name of the summary function used (e.g., importance_score_mean when fun = mean). The output is sorted in descending order of the summary importance scores.

Details

This method extends stats::aggregate for objects of class model_imp_tbl.