ML.KNN.CrossValidate

Package ML :: Package KNN :: Module CrossValidate |
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# Module CrossValidate

source code

    handles doing cross validation with k-nearest neighbors model

    and evaluation of individual models

Functions | [hide private]
makeClassificationModel(numNeigh, attrs, distFunc) | source code
makeRegressionModel(numNeigh, attrs, distFunc) | source code
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. | source code
CrossValidationDriver(examples, attrs, nPossibleValues, numNeigh, modelBuilder=<function makeClassificationModel at 0x9a8c8ec>, distFunc=<function EuclideanDist at 0x9a5ff0c>, holdOutFrac=0.3, silent=0, calcTotalError=0, **kwargs) Driver function for building a KNN model of a specified type... | source code

Function Details | [hide private]

### CrossValidate(knnMod, testExamples, appendExamples=0) | source code
    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:

### CrossValidationDriver(examples, attrs, nPossibleValues, numNeigh, modelBuilder=<function makeClassificationModel at 0x9a8c8ec>, distFunc=<function EuclideanDist at 0x9a5ff0c>, holdOutFrac=0.3, silent=0, calcTotalError=0, **kwargs) | source code
    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)