classification: Decision tree
In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

classification

R Documentation

Decision tree

Description

Decision tree

Usage

classification(
  data,
  dep,
  indep,
  testSize = 0.33,
  noOfFolds = 10,
  testing,
  reporting = list("classifMetrices"),
  classifier,
  minSplit = 20,
  minBucket = 0,
  complecity = 0.01,
  maxCompete = 4,
  maxSurrogate = 5,
  unsurrogate = 2,
  noCrossValidations = 10,
  maxDepth = 30,
  noOfTrees = 10,
  maxDepthRandFor = 30,
  sampleFraction = 1,
  splitRule,
  plotDecisionTree = FALSE,
  predictedFreq = FALSE,
  printRandForest = FALSE,
  predictedFreqRF = FALSE
)

Arguments

`data`	.
`dep`	.
`indep`	.
`testSize`	.
`noOfFolds`	.
`testing`	.
`reporting`	.
`classifier`	.
`minSplit`	.
`minBucket`	.
`complecity`	.
`maxCompete`	.
`maxSurrogate`	.
`unsurrogate`	.
`noCrossValidations`	.
`maxDepth`	.
`noOfTrees`	.
`maxDepthRandFor`	.
`sampleFraction`	.
`splitRule`	.
`plotDecisionTree`	.
`predictedFreq`	.
`printRandForest`	.
`predictedFreqRF`	.

Value

A results object containing:

`results$modelSettings`					a html
`results$confusion$matrix`					a table
`results$classificationMetrics$general`					a table
`results$classificationMetrics$class`					a table
`results$rocCurvePlot`					an image
`results$decisionTreeModel`					an image
`results$predictedFreqPlot`					an image
`results$printRandForest$randomForestModel`					a table
`results$text`					a preformatted