if the training data is a C++ dataset (e.g. SparseDataSet)
classification is much faster since everything is done in C++; if a
python container is used then it's a slower pure python
implementation.
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classify(self,
data,
i)
For each class the sum of the distances to the k nearest neighbors is
computed. |
source code
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cv(classifier,
data,
numFolds=5,
**args)
perform k-fold cross validation |
source code
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nCV(classifier,
data,
**args)
runs CV n times, returning a 'ResultsList' object. |
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project(self,
data)
project a test dataset to the training data features. |
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stratifiedCV(classifier,
data,
numFolds=5,
**args)
perform k-fold stratified cross-validation; in each fold the number of
patterns from each class is proportional to the relative fraction of the
class in the dataset |
source code
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trainTest(classifierTemplate,
data,
trainingPatterns,
testingPatterns,
**args)
Train a classifier on the list of training patterns, and test it
on the test patterns |
source code
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