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?"

resampling_plan_project <- drake_plan(
  resampl_inner = target(makeResampleDesc("CV", fixed = TRUE)),
  resamp_outer = target(makeResampleDesc("CV", fixed = TRUE)),
)

pat-s/2019-feature-selection documentation built on Dec. 24, 2021, 8:40 a.m.

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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?"

code/060-modeling/project/03-resampling.R
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?"

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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?"

code/060-modeling/project/03-resampling.R 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?"

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We want your feedback!

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?"

code/060-modeling/project/03-resampling.R
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?"