Trees | Indices | Help |
|
---|
|
_NaiveBayesClassifier_s can save the following pieces of internal state, accessible via standard setter/getter functions: 1) _Examples_: a list of examples which have been predicted 2) _TrainingExamples_: List of training examples - the descriptor value of these examples are quantized based on info gain using ML/Data/Quantize.py if necessary 3) _TestExamples_: the list of examples used to test the model 4) _BadExamples_ : list of examples that were incorrectly classified 4) _QBoundVals_: Quant bound values for each varaible - a list of lists 5) _QBounds_ : Number of bounds for each variable
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|
We will assume at this point that the training examples have been set We have to estmate the conditional probabilities for each of the (binned) descriptor component give a outcome (or class). Also the probabilities for each class is estimated |
Classify an example by summing over the conditional probabilities The most likely class is the one with the largest probability |
Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Thu Feb 1 16:13:01 2018 | http://epydoc.sourceforge.net |