rdkit.ML.NaiveBayes.ClassificationModel module

Defines Naive Baysean classification model Based on development in: Chapter 6 of “Machine Learning” by Tom Mitchell

class rdkit.ML.NaiveBayes.ClassificationModel.NaiveBayesClassifier(attrs, nPossibleVals, nQuantBounds, mEstimateVal=-1.0, useSigs=False)

Bases: object

_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

ClassifyExample(example, appendExamples=0)

Classify an example by summing over the conditional probabilities The most likely class is the one with the largest probability

ClassifyExamples(examples, appendExamples=0)

GetBadExamples()

GetClassificationDetails()

returns the probability of the last prediction

GetExamples()

GetName()

GetTestExamples()

GetTrainingExamples()

NameModel(varNames)

SetBadExamples(examples)

SetExamples(examples)

SetName(name)

SetTestExamples(examples)

SetTrainingExamples(examples)

trainModel()

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