Uses of Class
com.imsl.datamining.PredictiveModel.PredictiveModelException

Packages that use PredictiveModel.PredictiveModelException

Package	Description
com.imsl.datamining	Data mining and machine learning.
com.imsl.datamining.decisionTree	Decision trees.
com.imsl.datamining.supportvectormachine	Support vector machines.

Uses of PredictiveModel.PredictiveModelException in com.imsl.datamining

Subclasses of PredictiveModel.PredictiveModelException in com.imsl.datamining

Modifier and Type	Class	Description
static class	PredictiveModel.CloneNotSupportedException	Wraps the java.lang.CloneNotSupportedException to indicate that the clone method in class Object has been called to clone an object, but that the object's class does not implement the Cloneable interface.
static class	PredictiveModel.StateChangeException	Exception thrown when an input parameter has changed that might affect the model estimates or predictions.
static class	PredictiveModel.SumOfProbabilitiesNotOneException	Exception thrown when the sum of probabilities is not approximately one.

Methods in com.imsl.datamining that throw PredictiveModel.PredictiveModelException

Modifier and Type	Method	Description
void	BootstrapAggregation.aggregate()	Performs the bootstrap aggregation.
void	CrossValidation.crossValidate()	Performs V-Fold cross-validation.
void	GradientBoosting.fitModel()	Performs the gradient boosting on the training data.
void	LogisticRegression.fitModel()	Fits the logistic regression predictive model.
void	PredictiveModel.fitModel()	Fits the predictive model to the training data (estimates the model using the training data and current configuration settings).
double[]	GradientBoosting.predict()	Returns the predicted values on the training data.
double[]	GradientBoosting.predict(double[][] testData)	Returns the predicted values on the input test data.
double[]	GradientBoosting.predict(double[][] testData, double[] testDataWeights)	Runs the gradient boosting on the training data and returns the predicted values on the weighted test data.
double[]	GradientBoostingModelObject.predict(double[][] testData)	Returns the predictions on the test data.
double[]	GradientBoostingModelObject.predict(double[][] testData, double[] testDataWeights)	Returns the predictions on the test data using data weights.
double[]	LogisticRegression.predict()	Returns the fitted values on the training data.
double[]	LogisticRegression.predict(double[][] testData)	Returns the predicted values on the test data.
double[]	LogisticRegression.predict(double[][] testData, double[] testDataWeights)	Returns predicted values on the test data using the given weights.
double[]	LogisticRegressionModelObject.predict(double[][] testData)	Returns the predictions on the test data.
double[]	LogisticRegressionModelObject.predict(double[][] testData, double[] testDataWeights)	Returns predictions on the given test data based on the given weights.
abstract double[]	PredictiveModel.predict()	Predicts the response variable using the most recent fit.
abstract double[]	PredictiveModel.predict(double[][] testData)	Predicts the response values using the most recent fit and the provided test data.
abstract double[]	PredictiveModel.predict(double[][] testData, double[] testDataWeights)	Predicts the response values using the most recent fit, the provided test data, and the test data case weights.
protected abstract void	PredictiveModel.setConfiguration(PredictiveModel pm)	Sets the configuration of PredictiveModel to that of the input model.

Constructors in com.imsl.datamining that throw PredictiveModel.PredictiveModelException

Modifier	Constructor	Description
	CrossValidation(PredictiveModel pm)	Creates a CrossValidation object.

Uses of PredictiveModel.PredictiveModelException in com.imsl.datamining.decisionTree

Subclasses of PredictiveModel.PredictiveModelException in com.imsl.datamining.decisionTree

Modifier and Type	Class	Description
static class	DecisionTree.MaxTreeSizeExceededException	Exception thrown when the maximum tree size has been exceeded.
static class	DecisionTree.PruningFailedToConvergeException	Exception thrown when pruning fails to converge.
static class	DecisionTree.PureNodeException	Exception thrown when attempting to split a node that is already pure (response variable is constant).
static class	RandomTrees.ReflectiveOperationException	Class that wraps exceptions thrown by reflective operations in core reflection.

Methods in com.imsl.datamining.decisionTree that throw PredictiveModel.PredictiveModelException

Modifier and Type	Method	Description
void	DecisionTree.fitModel()	Fits the decision tree.
void	RandomTrees.fitModel()	Fits the random forest to the training data.
double[]	RandomTrees.predict()	Returns the predicted values generated by the random forest on the training data.
double[]	RandomTrees.predict(double[][] testData)	Returns the predicted values on the input test data.
double[]	RandomTrees.predict(double[][] testData, double[] testDataWeights)	Returns the predicted values on the input test data and the test data weights.
protected void	RandomTrees.setConfiguration(PredictiveModel pm)	Sets the configuration of RandomTrees to that of the input model.

Uses of PredictiveModel.PredictiveModelException in com.imsl.datamining.supportvectormachine

Subclasses of PredictiveModel.PredictiveModelException in com.imsl.datamining.supportvectormachine

Modifier and Type	Class	Description
static class	SupportVectorMachine.ReflectiveOperationException	Class that wraps exceptions thrown by reflective operations in core reflection.

Methods in com.imsl.datamining.supportvectormachine that throw PredictiveModel.PredictiveModelException

Modifier and Type	Method	Description
void	SupportVectorMachine.fitModel()	Fits the model to the training data, i.e, trains the support vector machine.