subsample: Subsampling Imbalanced Data

In AlineTalhouk/splendid: SuPervised Learning ENsemble for Diagnostic IDentification

Subsampling Imbalanced Data

Description

Subsampling imbalanced data using up-sampling, down-sampling, or SMOTE.

Usage

subsample(
  data,
  class,
  sampling = c("none", "up", "down", "smote"),
  seed_samp = NULL
)

Arguments

data	data frame with rows as samples, columns as features
class	true/reference class vector used for supervised learning
sampling	the default is "none", in which no subsampling is performed. Other options include "up" (Up-sampling the minority class), "down" (Down-sampling the majority class), and "smote" (synthetic points for the minority class and down-sampling the majority class). Subsampling is only applicable to the training set.
seed_samp	random seed used for reproducibility in subsampling training sets for model generation

Details

To deal with class imbalances, we can subsample the data so that the class proportions are more uniform.

Value

A subsampled dataset where corresponding strata of class are more balanced. The resulting class variable is not included in the data output.

Author(s)

Derek Chiu

Examples

# Create imbalanced version of iris dataset
iris_imbal <- iris[1:130, ]

# Up-sampling
iris_up <- subsample(iris_imbal, iris_imbal$Species, sampling = "up")
nrow(iris_up)

# Down-sampling
iris_down <- subsample(iris_imbal, iris_imbal$Species, sampling = "down")
nrow(iris_down)

# SMOTE
iris_smote <- subsample(iris_imbal, iris_imbal$Species, sampling = "smote")
nrow(iris_smote)

AlineTalhouk/splendid documentation built on Feb. 23, 2024, 9:37 p.m.