rdkit.ML.KNN.CrossValidate module — The RDKit 2016.09.1 documentation

# rdkit.ML.KNN.CrossValidate module

handles doing cross validation with k-nearest neighbors model

and evaluation of individual models

`rdkit.ML.KNN.CrossValidate.` `CrossValidate`(knnMod, testExamples, appendExamples=0)

  Determines the classification error for the testExamples

  Arguments

  > - tree: a decision tree (or anything supporting a _ClassifyExample()_ method)
  > - testExamples: a list of examples to be used for testing
  > - appendExamples: a toggle which is passed along to the tree as it does the classification. The trees can use this to store the examples they classify locally.

  Returns

  > a 2-tuple consisting of:

`rdkit.ML.KNN.CrossValidate.` `CrossValidationDriver`(examples, attrs, nPossibleValues, numNeigh, modelBuilder=<function makeClassificationModel>, distFunc=<function EuclideanDist>, holdOutFrac=0.3, silent=0, calcTotalError=0, **kwargs)

  Driver function for building a KNN model of a specified type

  Arguments

  > - examples: the full set of examples
  > - numNeigh: number of neighbors for the KNN model (basically k in k-NN)
  > - knnModel: the type of KNN model (a classification vs regression model)
  > - holdOutFrac: the fraction of the data which should be reserved for the hold-out set (used to calculate error)
  > - silent: a toggle used to control how much visual noise this makes as it goes
  > - calcTotalError: a toggle used to indicate whether the classification error of the tree should be calculated using the entire data set (when true) or just the training hold out set (when false)

`rdkit.ML.KNN.CrossValidate.` `makeClassificationModel`(numNeigh, attrs, distFunc)

`rdkit.ML.KNN.CrossValidate.` `makeRegressionModel`(numNeigh, attrs, distFunc)