feature_imp_parallel: Parallel feature importance wrapper
In pat-s/2019-feature-selection: Research Compendium package for the publication "Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?"
Description
Calculates feature importance via permutation
Usage
1
feature_imp_parallel(task, learner, nmc, measure)
Arguments
task
mlr task
learner
Learner from mlr::makeLearner
nmc
Number of Monte Carlo iterations
measure
Measure for scoring.
pat-s/2019-feature-selection documentation built on Dec. 24, 2021, 8:40 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
Calculates feature importance via permutation
Usage
1 | feature_imp_parallel(task, learner, nmc, measure)
|
Arguments
task |
mlr task |
learner |
Learner from mlr::makeLearner |
nmc |
Number of Monte Carlo iterations |
measure |
Measure for scoring. |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.