variable_importance_split: Compute variable importance according to the machine learning...

In cjubin/learnMET: Predictions with Machine Learning methods in Multi Environment Trials (MET)

Compute variable importance according to the machine learning algorithm used

Description

Variable importance can be calculated based on model-specific and model-agnostic approaches

Usage

variable_importance_split(object, ...)

## Default S3 method:
variable_importance_split(object, ...)

## S3 method for class 'fitted_DL_reg_1'
variable_importance_split(object)

## S3 method for class 'fitted_DL_reg_2'
variable_importance_split(object)

## S3 method for class 'fitted_xgb_reg_1'
variable_importance_split(
  object,
  path_plot,
  type = "model_specific",
  permutations = 10,
  unseen_data = F
)

## S3 method for class 'fitted_xgb_reg_2'
variable_importance_split(object)

## S3 method for class 'fitted_stacking_reg_1'
variable_importance_split(object)

## S3 method for class 'fitted_stacking_reg_2'
variable_importance_split(object)

## S3 method for class 'fitted_stacking_reg_3'
variable_importance_split(object)

## S3 method for class 'fitted_rf_reg_1'
variable_importance_split(object)

## S3 method for class 'fitted_rf_reg_2'
variable_importance_split(object)

## S3 method for class 'fitted_rf_reg_2'
variable_importance_split(object)

## S3 method for class 'fitted_rf_reg_1'
variable_importance_split(object)

Arguments

object	an object of class res_object
type	model_specific or model_agnostic
permutations	By default, equal to 10.
unseen_data

Author(s)

Cathy C. Westhues [e-mail]

References

\insertRef

breiman2001randomlearnMET \insertRefmolnar2022learnMET

cjubin/learnMET documentation built on Nov. 4, 2024, 6:23 p.m.