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MonadicContextualClassification

Guides

  • Classification pipeline functions

Tech Notes

  • Classification of MNIST data
  • Classification workflows monad

Symbols

  • ClConAccuracyByVariableShuffling
  • ClConAssignVariableNames
  • ClConBind
  • ClConClassifierMeasurementsByThreshold
  • ClConClassifierMeasurements
  • ClConContexts
  • ClConDropFromContext
  • ClConEchoContext
  • ClConEchoDataSummary
  • ClConEchoFunctionContext
  • ClConEchoFunctionValue
  • ClConEcho
  • ClConEchoValue
  • ClConEchoVariableNames
  • ClConGetVariableNames
  • ClConMakeClassifier
  • ClCon
  • ClConOutlierPosition
  • ClConRecoverData
  • ClConReduceDimension
  • ClConROCData
  • ClConROCListLinePlot
  • ClConROCPlot
  • ClConSetClassifier
  • ClConSetContext
  • ClConSetData
  • ClConSetTestData
  • ClConSetTrainingData
  • ClConSetValidationData
  • ClConSetValue
  • ClConSetVariableNames
  • ClConSplitData
  • ClConSuggestROCThresholds
  • ClConSummarizeDataLongForm
  • ClConSummarizeData
  • ClConTakeClassifier
  • ClConTakeContext
  • ClConTakeData
  • ClConTakeROCData
  • ClConTakeTestData
  • ClConTakeTrainingData
  • ClConTakeValidationData
  • ClConTakeValue
  • ClConTakeVariableNames
  • ClConToNormalClassifierData
  • ClConTrainClassifier
  • ClConUnit
  • ClConUnitQ
  • $ClConFailure
Classification pipeline functions
A monadic pipeline is a way to navigate computations in monad's function space. The unit function and the setter functions "uplift" data into monad's space. The taker functions "download" data from monad's space. The central -- and most computationally intensive -- functions are the classifier training and measurement functions. Variety of use cases are addressed with the rest of the functions. Diagnosis with Receiver Operating Characteristic (ROC) functions are extensively supported.
Fundamental
ClCon
— monad objects head
ClConUnit
— create monad unit object
$ClConFailure
— monad's failure symbol
ClConUnitQ
— check whether an expression is a monad object
ClConBind
— monad's binding function
Setters
ClConSetContext
▪
ClConSetValue
▪
ClConSetData
▪
ClConSetTrainingData
▪
ClConSetTestData
▪
ClConSetValidationData
▪
ClConSetVariableNames
Takers
ClConTakeContext
▪
ClConTakeValue
▪
ClConData
▪
ClConTakeTrainingData
▪
ClConTakeTestData
▪
ClConTakeValidationData
▪
ClConTakeVariableNames
▪
ClConTakeROCData
▪
ClConTakeClassifier
Removers
ClConDropFromContext
▪
ClConOutlierPosition
Preliminary
ClConEchoDataSummary
▪
ClConEchoVariableNames
▪
ClConReduceDimension
Classification
ClConMakeClassifier
— make a classifier (with given algorithm names and options)
ClConSuggestROCThresholds
— suggest classification thresholds based on ROC data
Classifier measurements
ClConClassifierMeasurements
— computes specified measurements
ClConClassifierMeasurementsByThreshold
— computes specified measurements using a threshold for a specified class label
ClConROCData
— computes ROC plot data
ClConROCPlot
— plots ROC curves
ClConROCListLinePlot
— plots ROC functions using
ListLinePlot
Importance of variables
ClConAccuracyByVariableShuffling
— computes variable importance
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