Uses of Class
com.imsl.datamining.PredictiveModel.PredictiveModelException
Packages that use PredictiveModel.PredictiveModelException
Package
Description
Data mining and machine learning.
Decision trees.
Support vector machines.
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Uses of PredictiveModel.PredictiveModelException in com.imsl.datamining
Subclasses of PredictiveModel.PredictiveModelException in com.imsl.dataminingModifier and TypeClassDescriptionstatic classWraps thejava.lang.CloneNotSupportedExceptionto indicate that theclonemethod in classObjecthas been called to clone an object, but that the object's class does not implement theCloneableinterface.static classException thrown when an input parameter has changed that might affect the model estimates or predictions.static classException thrown when the sum of probabilities is not approximately one.Methods in com.imsl.datamining that throw PredictiveModel.PredictiveModelExceptionModifier and TypeMethodDescriptionvoidBootstrapAggregation.aggregate()Performs the bootstrap aggregation.voidCrossValidation.crossValidate()Performs V-Fold cross-validation.voidGradientBoosting.fitModel()Performs the gradient boosting on the training data.voidLogisticRegression.fitModel()Fits the logistic regression predictive model.voidPredictiveModel.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 voidPredictiveModel.setConfiguration(PredictiveModel pm) Sets the configuration ofPredictiveModelto that of the input model.Constructors in com.imsl.datamining that throw PredictiveModel.PredictiveModelExceptionModifierConstructorDescriptionCreates aCrossValidationobject. -
Uses of PredictiveModel.PredictiveModelException in com.imsl.datamining.decisionTree
Subclasses of PredictiveModel.PredictiveModelException in com.imsl.datamining.decisionTreeModifier and TypeClassDescriptionstatic classException thrown when the maximum tree size has been exceeded.static classException thrown when pruning fails to converge.static classException thrown when attempting to split a node that is already pure (response variable is constant).static classClass that wraps exceptions thrown by reflective operations in core reflection.Methods in com.imsl.datamining.decisionTree that throw PredictiveModel.PredictiveModelExceptionModifier and TypeMethodDescriptionvoidDecisionTree.fitModel()Fits the decision tree.voidRandomTrees.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 voidRandomTrees.setConfiguration(PredictiveModel pm) Sets the configuration ofRandomTreesto that of the input model. -
Uses of PredictiveModel.PredictiveModelException in com.imsl.datamining.supportvectormachine
Modifier and TypeClassDescriptionstatic classClass that wraps exceptions thrown by reflective operations in core reflection.Methods in com.imsl.datamining.supportvectormachine that throw PredictiveModel.PredictiveModelExceptionModifier and TypeMethodDescriptionvoidSupportVectorMachine.fitModel()Fits the model to the training data, i.e, trains the support vector machine.