ML.KNN.CrossValidate

Module CrossValidate

handles doing cross validation with k-nearest neighbors model and evaluation of individual models

Functions

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)