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)