Uses of Class
com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException
Packages that use PredictiveModel.SumOfProbabilitiesNotOneException
Package
Description
Data mining and machine learning.
Decision trees.
Support vector machines.
-
Uses of PredictiveModel.SumOfProbabilitiesNotOneException in com.imsl.datamining
Methods in com.imsl.datamining that throw PredictiveModel.SumOfProbabilitiesNotOneExceptionModifier and TypeMethodDescriptionvoidPredictiveModel.setClassProbabilities(double[][] probs) Sets the class probabilities.voidPredictiveModel.setPriorProbabilities(double[] priors) Sets the prior probabilities for class membership. -
Uses of PredictiveModel.SumOfProbabilitiesNotOneException in com.imsl.datamining.decisionTree
Methods in com.imsl.datamining.decisionTree that throw PredictiveModel.SumOfProbabilitiesNotOneException -
Uses of PredictiveModel.SumOfProbabilitiesNotOneException in com.imsl.datamining.supportvectormachine
Methods in com.imsl.datamining.supportvectormachine that throw PredictiveModel.SumOfProbabilitiesNotOneExceptionModifier and TypeMethodDescriptiondouble[]SupportVectorMachine.predict()Returns the predicted values on the training data, i.e., returns the fitted values.double[]SupportVectorMachine.predict(double[][] testData) Returns the predicted values on the input test data.protected abstract double[]SupportVectorMachine.predictValues(SVModel model, double[][] attributeData) Abstract method for generating the predicted values using the fitted support vector machine model.protected double[]SVClassification.predictValues(SVModel model, double[][] attributeData) Generates the predicted values on the attribute data using the given support vector machine model.protected double[]SVOneClass.predictValues(SVModel model, double[][] attributeData) Generates the predicted values on the attribute data using the given support vector machine model.protected double[]SVRegression.predictValues(SVModel model, double[][] attributeData) Generates the predicted values on the attribute data using the given support vector machine model.