permutation_based_vip: 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 calculated with permutation approach. The procedure is applied feature-wise:
the values for the considered feature are permuted. Then, using the predictions of the permuted data, we estimate the error with this dataset from the original

Usage

permutation_based_vip(model, x, y, permutations, predictors, path_plot)

Arguments

permutations	Number of permutations to compute VI
predictors	Set of predictors for which the variable importance is estimated via permutations.
object	an object of class res_fitted_split
unseen_data	Logical indicating whether the VI is estimated using the test set. By default, training set is used.

Value

data.frame with variable importance based on permutations

Author(s)

Cathy C. Westhues [e-mail]

References

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fisher2019alllearnMET

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