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 0x9c93a3c>, distFunc=<function EuclideanDist at 0x9c93534>, holdOutFrac=0.3, silent=0, calcTotalError=0, **kwargs) Driver function for building a KNN model of a specified type... | source code

Variables | [hide private]
| Complex0 = ``'``F``'``
| Complex16 = ``'``F``'``
| Complex32 = ``'``F``'``
| Complex64 = ``'``D``'``
| Complex8 = ``'``F``'``
| Float0 = ``'``f``'``
| Float16 = ``'``f``'``
| Float32 = ``'``f``'``
| Float64 = ``'``d``'``
| Float8 = ``'``f``'``
| Int0 = ``'``1``'``
| Int16 = ``'``s``'``
| Int32 = ``'``i``'``
| Int8 = ``'``1``'``
| absolute = `<ufunc 'absolute'>`
| add = `<ufunc 'add'>`
| arccos = `<ufunc 'arccos'>`
| arccosh = `<ufunc 'arccosh'>`
| arcsin = `<ufunc 'arcsin'>`
| arcsinh = `<ufunc 'arcsinh'>`
| arctan = `<ufunc 'arctan'>`
| arctan2 = `<ufunc 'arctan2'>`
| arctanh = `<ufunc 'arctanh'>`
| bitwise_and = `<ufunc 'bitwise_and'>`
| bitwise_or = `<ufunc 'bitwise_or'>`
| bitwise_xor = `<ufunc 'bitwise_xor'>`
| ceil = `<ufunc 'ceil'>`
| conjugate = `<ufunc 'conjugate'>`
| cos = `<ufunc 'cos'>`
| cosh = `<ufunc 'cosh'>`
| divide = `<ufunc 'divide'>`
| divide_safe = `<ufunc 'divide_safe'>`
| e = `2.71828182846`
| equal = `<ufunc 'equal'>`
| exp = `<ufunc 'exp'>`
| fabs = `<ufunc 'fabs'>`
| floor = `<ufunc 'floor'>`
| floor_divide = `<ufunc 'floor_divide'>`
| fmod = `<ufunc 'fmod'>`
| greater = `<ufunc 'greater'>`
| greater_equal = `<ufunc 'greater_equal'>`
| hypot = `<ufunc 'hypot'>`
| invert = `<ufunc 'invert'>`
| left_shift = `<ufunc 'left_shift'>`
| less = `<ufunc 'less'>`
| less_equal = `<ufunc 'less_equal'>`
| log = `<ufunc 'log'>`
| log10 = `<ufunc 'log10'>`
| logical_and = `<ufunc 'logical_and'>`
| logical_not = `<ufunc 'logical_not'>`
| logical_or = `<ufunc 'logical_or'>`
| logical_xor = `<ufunc 'logical_xor'>`
| maximum = `<ufunc 'maximum'>`
| minimum = `<ufunc 'minimum'>`
| multiply = `<ufunc 'multiply'>`
| negative = `<ufunc 'negative'>`
| not_equal = `<ufunc 'not_equal'>`
| pi = `3.14159265359`
| power = `<ufunc 'power'>`
| remainder = `<ufunc 'remainder'>`
| right_shift = `<ufunc 'right_shift'>`
| sin = `<ufunc 'sin'>`
| sinh = `<ufunc 'sinh'>`
| sqrt = `<ufunc 'sqrt'>`
| subtract = `<ufunc 'subtract'>`
| tan = `<ufunc 'tan'>`
| tanh = `<ufunc 'tanh'>`
| true_divide = `<ufunc 'true_divide'>`

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 0x9c93a3c>, distFunc=<function EuclideanDist at 0x9c93534>, 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)