simulate: Title Simulates random splits and performs hypothesis testing

In eklavyaj/RandomSplitDiagnostics: The Package allows you to diagnose the randomness and model performance of a given train-test split in machine learning.

Title Simulates random splits and performs hypothesis testing

Description

Title Simulates random splits and performs hypothesis testing

Usage

simulate(
  n.simulation,
  dataset.name,
  df.train,
  df.test,
  num.simulations,
  model.relation,
  split.percentage,
  initial.scores = c(),
  alpha,
  save.plots = TRUE,
  output.dir,
  metric.performance = "Normalized AIC"
)

Arguments

dataset.name	Name of the Dataset (String)
df.train	Train Partition (R DataFrame)
df.test	Test Partition (R DataFrame)
num.simulations	Number of simulations, defaults to 200
model.relation	The relation used for regression model
split.percentage	Ratio of the number of train entries to total entries
initial.scores	The performance metric value for the given initial train-test split
alpha	The level of the test for visualize_threshold, default set to 0.05
save.plots	Saves plots in output.dir when set to TRUE
output.dir	The path to output directory the plots are saved to
metric.performance	The performance metric, usually Normalized AIC

eklavyaj/RandomSplitDiagnostics documentation built on June 1, 2022, 8:36 p.m.