Uses of Class
org.tribuo.regression.Regressor

Packages that use Regressor

Package	Description
org.tribuo.classification.explanations.lime	Provides an implementation of LIME (Locally Interpretable Model Explanations).
org.tribuo.interop.onnx	This package contains a Tribuo wrapper around the ONNX Runtime.
org.tribuo.interop.tensorflow	Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
org.tribuo.regression	Provides classes and infrastructure for regression problems with single or multiple output dimensions.
org.tribuo.regression.baseline	Provides simple baseline regression predictors.
org.tribuo.regression.ensemble	Provides EnsembleCombiner implementations for working with multi-output regression problems.
org.tribuo.regression.evaluation	Evaluation classes for single or multi-dimensional regression.
org.tribuo.regression.example	Provides some example regression data generators for testing implementations.
org.tribuo.regression.impl	Provides skeletal implementations of Regressor Trainer that can wrap a single dimension trainer/model and produce one prediction per dimension independently.
org.tribuo.regression.liblinear	Provides an interface to liblinear for regression problems.
org.tribuo.regression.libsvm	Provides an interface to LibSVM for regression problems.
org.tribuo.regression.rtree	Provides an implementation of decision trees for regression problems.
org.tribuo.regression.rtree.impl	Provides internal implementation classes for the regression trees.
org.tribuo.regression.sgd.linear	Provides an implementation of linear regression using Stochastic Gradient Descent.
org.tribuo.regression.slm	Provides implementations of sparse linear regression using various forms of regularisation penalty.
org.tribuo.regression.xgboost	Provides an interface to XGBoost for regression problems.

Uses of Regressor in org.tribuo.classification.explanations.lime

Fields in org.tribuo.classification.explanations.lime with type parameters of type Regressor

Modifier and Type	Field and Description
protected SparseTrainer<Regressor>	LIMEBase.explanationTrainer
protected static OutputFactory<Regressor>	LIMEBase.regressionFactory

Methods in org.tribuo.classification.explanations.lime that return Regressor

Modifier and Type	Method and Description
static Regressor	LIMEBase.transformOutput(Prediction<Label> prediction) Transforms a Prediction for a multiclass problem into a Regressor output which represents the probability for each class.

Methods in org.tribuo.classification.explanations.lime that return types with arguments of type Regressor

Modifier and Type	Method and Description
protected com.oracle.labs.mlrg.olcut.util.Pair<LIMEExplanation,List<Example<Regressor>>>	LIMEBase.explainWithSamples(Example<Label> example)
protected com.oracle.labs.mlrg.olcut.util.Pair<LIMEExplanation,List<Example<Regressor>>>	LIMEColumnar.explainWithSamples(Map<String,String> input)
SparseModel<Regressor>	LIMEExplanation.getModel()
protected List<Example<Regressor>>	LIMEText.sampleData(String inputText, List<Token> tokens) Samples a new dataset from the input text.
protected SparseModel<Regressor>	LIMEBase.trainExplainer(Example<Regressor> target, List<Example<Regressor>> samples) Trains the explanation model using the supplied sampled data and the input example.

Method parameters in org.tribuo.classification.explanations.lime with type arguments of type Regressor

Modifier and Type	Method and Description
protected SparseModel<Regressor>	LIMEBase.trainExplainer(Example<Regressor> target, List<Example<Regressor>> samples) Trains the explanation model using the supplied sampled data and the input example.
protected SparseModel<Regressor>	LIMEBase.trainExplainer(Example<Regressor> target, List<Example<Regressor>> samples) Trains the explanation model using the supplied sampled data and the input example.

Constructor parameters in org.tribuo.classification.explanations.lime with type arguments of type Regressor

Constructor and Description
LIMEBase(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples) Constructs a LIME explainer for a model which uses tabular data (i.e., no special treatment for text features).
LIMEColumnar(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, RowProcessor<Label> exampleGenerator, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses the columnar data processing system.
LIMEExplanation(SparseModel<Regressor> model, Prediction<Label> prediction, RegressionEvaluation evaluation)
LIMEText(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, TextFeatureExtractor<Label> extractor, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses text data.

Uses of Regressor in org.tribuo.interop.onnx

Methods in org.tribuo.interop.onnx that return Regressor

Modifier and Type	Method and Description
Regressor	RegressorTransformer.transformToOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo)

Methods in org.tribuo.interop.onnx that return types with arguments of type Regressor

Modifier and Type	Method and Description
List<Regressor>	RegressorTransformer.transformToBatchOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo)
List<Prediction<Regressor>>	RegressorTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples)
Prediction<Regressor>	RegressorTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example)

Method parameters in org.tribuo.interop.onnx with type arguments of type Regressor

Modifier and Type	Method and Description
List<Regressor>	RegressorTransformer.transformToBatchOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo)
List<Prediction<Regressor>>	RegressorTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples)
List<Prediction<Regressor>>	RegressorTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples)
Regressor	RegressorTransformer.transformToOutput(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo)
Prediction<Regressor>	RegressorTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example)
Prediction<Regressor>	RegressorTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example)

Uses of Regressor in org.tribuo.interop.tensorflow

Methods in org.tribuo.interop.tensorflow that return Regressor

Modifier and Type	Method and Description
Regressor	RegressorConverter.convertToOutput(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo)

Methods in org.tribuo.interop.tensorflow that return types with arguments of type Regressor

Modifier and Type	Method and Description
List<Regressor>	RegressorConverter.convertToBatchOutput(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo)
List<Prediction<Regressor>>	RegressorConverter.convertToBatchPrediction(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples)
Prediction<Regressor>	RegressorConverter.convertToPrediction(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example)

Methods in org.tribuo.interop.tensorflow with parameters of type Regressor

Modifier and Type	Method and Description
org.tensorflow.Tensor	RegressorConverter.convertToTensor(Regressor example, ImmutableOutputInfo<Regressor> outputIDInfo)

Method parameters in org.tribuo.interop.tensorflow with type arguments of type Regressor

Modifier and Type	Method and Description
List<Regressor>	RegressorConverter.convertToBatchOutput(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo)
List<Prediction<Regressor>>	RegressorConverter.convertToBatchPrediction(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples)
List<Prediction<Regressor>>	RegressorConverter.convertToBatchPrediction(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples)
Regressor	RegressorConverter.convertToOutput(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo)
Prediction<Regressor>	RegressorConverter.convertToPrediction(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example)
Prediction<Regressor>	RegressorConverter.convertToPrediction(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example)
org.tensorflow.Tensor	RegressorConverter.convertToTensor(List<Example<Regressor>> examples, ImmutableOutputInfo<Regressor> outputIDInfo)
org.tensorflow.Tensor	RegressorConverter.convertToTensor(List<Example<Regressor>> examples, ImmutableOutputInfo<Regressor> outputIDInfo)
org.tensorflow.Tensor	RegressorConverter.convertToTensor(Regressor example, ImmutableOutputInfo<Regressor> outputIDInfo)

Uses of Regressor in org.tribuo.regression

Subclasses of Regressor in org.tribuo.regression

Modifier and Type	Class and Description
static class	Regressor.DimensionTuple A Regressor which contains a single dimension, used internally when the model implementation doesn't natively support multi-dimensional regression outputs.

Fields in org.tribuo.regression declared as Regressor

Modifier and Type	Field and Description
static Regressor	RegressionFactory.UNKNOWN_MULTIPLE_REGRESSOR Deprecated. Deprecated when regression was made multidimensional by default. Use RegressionFactory.UNKNOWN_REGRESSOR instead.
static Regressor	RegressionFactory.UNKNOWN_REGRESSOR The sentinel unknown regressor, used when there is no ground truth regressor value.

Methods in org.tribuo.regression that return Regressor

Modifier and Type	Method and Description
Regressor	Regressor.copy()
static Regressor	Regressor.createFromPairList(List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>> dimensions) Creates a Regressor from a list of dimension tuples.
<V> Regressor	RegressionFactory.generateOutput(V label) Parses the Regressor value either by toStringing the input and calling parseString(java.lang.String) or if it's a collection iterating over the elements calling toString on each element in turn and using parseElement(int, java.lang.String).
Regressor	ImmutableRegressionInfo.getOutput(int id)
Regressor	RegressionFactory.getUnknownOutput()
static Regressor	Regressor.parseString(String s) Parses a string of the form:
static Regressor	Regressor.parseString(String s, char splitChar) Parses a string of the form:

Methods in org.tribuo.regression that return types with arguments of type Regressor

Modifier and Type	Method and Description
ImmutableOutputInfo<Regressor>	RegressionFactory.constructInfoForExternalModel(Map<Regressor,Integer> mapping)
ImmutableOutputInfo<Regressor>	RegressionInfo.generateImmutableOutputInfo()
MutableOutputInfo<Regressor>	RegressionFactory.generateInfo()
MutableOutputInfo<Regressor>	RegressionInfo.generateMutableOutputInfo()
Set<Regressor>	RegressionInfo.getDomain() Returns a set containing a Regressor for each dimension with the minimum value observed.
Set<Regressor>	ImmutableRegressionInfo.getDomain()
Evaluator<Regressor,RegressionEvaluation>	RegressionFactory.getEvaluator()
Iterator<com.oracle.labs.mlrg.olcut.util.Pair<Integer,Regressor>>	ImmutableRegressionInfo.iterator()

Methods in org.tribuo.regression with parameters of type Regressor

Modifier and Type	Method and Description
boolean	Regressor.fullEquals(Regressor other)
boolean	Regressor.DimensionTuple.fullEquals(Regressor other)
int	ImmutableRegressionInfo.getID(Regressor output)
void	MutableRegressionInfo.observe(Regressor output)

Method parameters in org.tribuo.regression with type arguments of type Regressor

Modifier and Type	Method and Description
ImmutableOutputInfo<Regressor>	RegressionFactory.constructInfoForExternalModel(Map<Regressor,Integer> mapping)
static String[]	Regressor.extractNames(OutputInfo<Regressor> info) Extracts the names from the supplied Regressor domain in their canonical order.

Uses of Regressor in org.tribuo.regression.baseline

Methods in org.tribuo.regression.baseline that return types with arguments of type Regressor

Modifier and Type	Method and Description
protected Model<Regressor>	DummyRegressionModel.copy(String newName, ModelProvenance newProvenance)
Optional<Excuse<Regressor>>	DummyRegressionModel.getExcuse(Example<Regressor> example)
Prediction<Regressor>	DummyRegressionModel.predict(Example<Regressor> example)

Method parameters in org.tribuo.regression.baseline with type arguments of type Regressor

Modifier and Type	Method and Description
Optional<Excuse<Regressor>>	DummyRegressionModel.getExcuse(Example<Regressor> example)
Prediction<Regressor>	DummyRegressionModel.predict(Example<Regressor> example)
DummyRegressionModel	DummyRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Uses of Regressor in org.tribuo.regression.ensemble

Methods in org.tribuo.regression.ensemble that return types with arguments of type Regressor

Modifier and Type	Method and Description
Prediction<Regressor>	AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions)
Prediction<Regressor>	AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions, float[] weights)

Method parameters in org.tribuo.regression.ensemble with type arguments of type Regressor

Modifier and Type	Method and Description
Prediction<Regressor>	AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions)
Prediction<Regressor>	AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions)
Prediction<Regressor>	AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions, float[] weights)
Prediction<Regressor>	AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions, float[] weights)

Uses of Regressor in org.tribuo.regression.evaluation

Methods in org.tribuo.regression.evaluation that return types with arguments of type Regressor

Modifier and Type	Method and Description
Map<Regressor,Double>	RegressionEvaluation.explainedVariance() Calculatest the explained variance for all dimensions.
MetricTarget<Regressor>	RegressionMetric.getTarget()
Map<Regressor,Double>	RegressionEvaluation.mae() Calculates the Mean Absolute Error for all dimensions.
Map<Regressor,Double>	RegressionEvaluation.r2() Calculates R2 for all dimensions.
Map<Regressor,Double>	RegressionEvaluation.rmse() Calculates the RMSE for all dimensions.

Methods in org.tribuo.regression.evaluation with parameters of type Regressor

Modifier and Type	Method and Description
double	RegressionEvaluation.explainedVariance(Regressor variable) Calculates the explained variance of the ground truth using the predictions for the supplied dimension.
static double	RegressionMetrics.explainedVariance(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the explained variance based on the supplied statistics for a single dimension.
double	RegressionEvaluation.mae(Regressor variable) Calculates the Mean Absolute Error for that dimension.
static double	RegressionMetrics.mae(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the Mean Absolute Error based on the supplied statistics for a single dimension.
double	RegressionEvaluation.r2(Regressor variable) Calculates R2 for the supplied dimension.
static double	RegressionMetrics.r2(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates R^2 based on the supplied statistics for a single dimension.
double	RegressionEvaluation.rmse(Regressor variable) Calculates the Root Mean Squared Error (i.e., the square root of the average squared errors across all data points) for the supplied dimension.
static double	RegressionMetrics.rmse(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the RMSE based on the supplied statistics for a single dimension.

Method parameters in org.tribuo.regression.evaluation with type arguments of type Regressor

Modifier and Type	Method and Description
org.tribuo.regression.evaluation.RegressionMetric.Context	RegressionMetric.createContext(Model<Regressor> model, List<Prediction<Regressor>> predictions)
org.tribuo.regression.evaluation.RegressionMetric.Context	RegressionMetric.createContext(Model<Regressor> model, List<Prediction<Regressor>> predictions)
protected org.tribuo.regression.evaluation.RegressionMetric.Context	RegressionEvaluator.createContext(Model<Regressor> model, List<Prediction<Regressor>> predictions)
protected org.tribuo.regression.evaluation.RegressionMetric.Context	RegressionEvaluator.createContext(Model<Regressor> model, List<Prediction<Regressor>> predictions)
protected RegressionEvaluation	RegressionEvaluator.createEvaluation(org.tribuo.regression.evaluation.RegressionMetric.Context context, Map<MetricID<Regressor>,Double> results, EvaluationProvenance provenance)
protected Set<RegressionMetric>	RegressionEvaluator.createMetrics(Model<Regressor> model)
static double	RegressionMetrics.explainedVariance(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the explained variance based on the supplied statistics.
static double	RegressionMetrics.mae(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the Mean Absolute Error based on the supplied statistics.
static double	RegressionMetrics.r2(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates R^2 based on the supplied statistics.
static double	RegressionMetrics.rmse(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the RMSE based on the supplied statistics.

Constructor parameters in org.tribuo.regression.evaluation with type arguments of type Regressor

Constructor and Description
RegressionMetric(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>,org.tribuo.regression.evaluation.RegressionMetric.Context> impl) Construct a new RegressionMetric for the supplied metric target, using the supplied function.
RegressionMetric(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>,org.tribuo.regression.evaluation.RegressionMetric.Context> impl) Construct a new RegressionMetric for the supplied metric target, using the supplied function.
RegressionMetric(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>,org.tribuo.regression.evaluation.RegressionMetric.Context> impl, boolean useExampleWeights) Construct a new RegressionMetric for the supplied metric target, using the supplied function.
RegressionMetric(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>,org.tribuo.regression.evaluation.RegressionMetric.Context> impl, boolean useExampleWeights) Construct a new RegressionMetric for the supplied metric target, using the supplied function.
RegressionSufficientStatistics(ImmutableOutputInfo<Regressor> domain, List<Prediction<Regressor>> predictions, boolean useExampleWeights)
RegressionSufficientStatistics(ImmutableOutputInfo<Regressor> domain, List<Prediction<Regressor>> predictions, boolean useExampleWeights)

Uses of Regressor in org.tribuo.regression.example

Methods in org.tribuo.regression.example that return types with arguments of type Regressor

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.denseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.denseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.denseTrainTest(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.denseTrainTest(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static Example<Regressor>	RegressionDataGenerator.emptyExample() Generates an example with no features.
static Example<Regressor>	RegressionDataGenerator.emptyMultiDimExample() Generates an example with no features.
static Dataset<Regressor>	NonlinearGaussianDataSource.generateDataset(int numSamples, float[] weights, float intercept, float variance, float xZeroMin, float xZeroMax, float xOneMin, float xOneMax, long seed) Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).
static Dataset<Regressor>	GaussianDataSource.generateDataset(int numSamples, float slope, float intercept, float variance, float xMin, float xMax, long seed) Generates a single dimensional output drawn from N(slope*x + intercept,variance).
OutputFactory<Regressor>	NonlinearGaussianDataSource.getOutputFactory()
OutputFactory<Regressor>	GaussianDataSource.getOutputFactory()
static Example<Regressor>	RegressionDataGenerator.invalidMultiDimSparseExample() Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
static Example<Regressor>	RegressionDataGenerator.invalidSparseExample() Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
Iterator<Example<Regressor>>	NonlinearGaussianDataSource.iterator()
Iterator<Example<Regressor>>	GaussianDataSource.iterator()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimDenseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimDenseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimDenseTrainTest(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimDenseTrainTest(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest() Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest() Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.threeDimDenseTrainTest(double negate, boolean remapIndices) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.threeDimDenseTrainTest(double negate, boolean remapIndices) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.

Uses of Regressor in org.tribuo.regression.impl

Methods in org.tribuo.regression.impl that return types with arguments of type Regressor

Modifier and Type	Method and Description
Prediction<Regressor>	SkeletalIndependentRegressionSparseModel.predict(Example<Regressor> example)
Prediction<Regressor>	SkeletalIndependentRegressionModel.predict(Example<Regressor> example)

Method parameters in org.tribuo.regression.impl with type arguments of type Regressor

Modifier and Type	Method and Description
protected SparseVector	SkeletalIndependentRegressionSparseModel.createFeatures(Example<Regressor> example) Creates the feature vector.
protected SparseVector	SkeletalIndependentRegressionModel.createFeatures(Example<Regressor> example) Creates the feature vector.
protected abstract SkeletalIndependentRegressionModel	SkeletalIndependentRegressionTrainer.createModel(Map<String,T> models, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.
protected abstract SkeletalIndependentRegressionSparseModel	SkeletalIndependentRegressionSparseTrainer.createModel(Map<String,T> models, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.
Prediction<Regressor>	SkeletalIndependentRegressionSparseModel.predict(Example<Regressor> example)
Prediction<Regressor>	SkeletalIndependentRegressionModel.predict(Example<Regressor> example)
SkeletalIndependentRegressionModel	SkeletalIndependentRegressionTrainer.train(Dataset<Regressor> examples)
SkeletalIndependentRegressionSparseModel	SkeletalIndependentRegressionSparseTrainer.train(Dataset<Regressor> examples)
SkeletalIndependentRegressionModel	SkeletalIndependentRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
SkeletalIndependentRegressionSparseModel	SkeletalIndependentRegressionSparseTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Constructor parameters in org.tribuo.regression.impl with type arguments of type Regressor

Constructor and Description
SkeletalIndependentRegressionModel(String name, String[] dimensions, ModelProvenance modelProvenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) models.size() must equal labelInfo.getDomain().size()
SkeletalIndependentRegressionSparseModel(String name, String[] dimensions, ModelProvenance modelProvenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo, Map<String,List<String>> activeFeatures) models.size() must equal labelInfo.getDomain().size()

Uses of Regressor in org.tribuo.regression.liblinear

Methods in org.tribuo.regression.liblinear that return types with arguments of type Regressor

Modifier and Type	Method and Description
protected LibLinearModel<Regressor>	LibLinearRegressionTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models)
protected Excuse<Regressor>	LibLinearRegressionModel.innerGetExcuse(Example<Regressor> e, double[][] allFeatureWeights) The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
Prediction<Regressor>	LibLinearRegressionModel.predict(Example<Regressor> example)

Method parameters in org.tribuo.regression.liblinear with type arguments of type Regressor

Modifier and Type	Method and Description
protected LibLinearModel<Regressor>	LibLinearRegressionTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models)
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearRegressionTrainer.extractData(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap)
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearRegressionTrainer.extractData(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap)
protected Excuse<Regressor>	LibLinearRegressionModel.innerGetExcuse(Example<Regressor> e, double[][] allFeatureWeights) The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
Prediction<Regressor>	LibLinearRegressionModel.predict(Example<Regressor> example)

Uses of Regressor in org.tribuo.regression.libsvm

Methods in org.tribuo.regression.libsvm that return types with arguments of type Regressor

Modifier and Type	Method and Description
protected LibSVMModel<Regressor>	LibSVMRegressionTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<libsvm.svm_model> models)
Prediction<Regressor>	LibSVMRegressionModel.predict(Example<Regressor> example)

Method parameters in org.tribuo.regression.libsvm with type arguments of type Regressor

Modifier and Type	Method and Description
protected LibSVMModel<Regressor>	LibSVMRegressionTrainer.createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<libsvm.svm_model> models)
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMRegressionTrainer.extractData(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap)
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMRegressionTrainer.extractData(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap)
Prediction<Regressor>	LibSVMRegressionModel.predict(Example<Regressor> example)

Constructor parameters in org.tribuo.regression.libsvm with type arguments of type Regressor

Constructor and Description
LibSVMRegressionTrainer(SVMParameters<Regressor> parameters) Constructs a LibSVMRegressionTrainer using the supplied parameters without standardizing the regression variables.
LibSVMRegressionTrainer(SVMParameters<Regressor> parameters, boolean standardize) Constructs a LibSVMRegressionTrainer using the supplied parameters and Trainer.DEFAULT_SEED.
LibSVMRegressionTrainer(SVMParameters<Regressor> parameters, boolean standardize, long seed) Constructs a LibSVMRegressionTrainer using the supplied parameters and seed.

Uses of Regressor in org.tribuo.regression.rtree

Methods in org.tribuo.regression.rtree that return types with arguments of type Regressor

Modifier and Type	Method and Description
Optional<Excuse<Regressor>>	IndependentRegressionTreeModel.getExcuse(Example<Regressor> example)
protected AbstractTrainingNode<Regressor>	CARTRegressionTrainer.mkTrainingNode(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
protected AbstractTrainingNode<Regressor>	CARTJointRegressionTrainer.mkTrainingNode(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
Prediction<Regressor>	IndependentRegressionTreeModel.predict(Example<Regressor> example)
TreeModel<Regressor>	CARTRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Method parameters in org.tribuo.regression.rtree with type arguments of type Regressor

Modifier and Type	Method and Description
Optional<Excuse<Regressor>>	IndependentRegressionTreeModel.getExcuse(Example<Regressor> example)
protected AbstractTrainingNode<Regressor>	CARTRegressionTrainer.mkTrainingNode(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
protected AbstractTrainingNode<Regressor>	CARTJointRegressionTrainer.mkTrainingNode(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
Prediction<Regressor>	IndependentRegressionTreeModel.predict(Example<Regressor> example)
TreeModel<Regressor>	CARTRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Regressor in org.tribuo.regression.rtree.impl

Methods in org.tribuo.regression.rtree.impl that return types with arguments of type Regressor

Modifier and Type	Method and Description
List<AbstractTrainingNode<Regressor>>	RegressorTrainingNode.buildTree(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
List<AbstractTrainingNode<Regressor>>	JointRegressorTrainingNode.buildTree(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
Node<Regressor>	RegressorTrainingNode.convertTree() Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
Node<Regressor>	JointRegressorTrainingNode.convertTree() Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

Method parameters in org.tribuo.regression.rtree.impl with type arguments of type Regressor

Modifier and Type	Method and Description
static RegressorTrainingNode.InvertedData	RegressorTrainingNode.invertData(Dataset<Regressor> examples) Inverts a training dataset from row major to column major.

Constructor parameters in org.tribuo.regression.rtree.impl with type arguments of type Regressor

Constructor and Description
JointRegressorTrainingNode(RegressorImpurity impurity, Dataset<Regressor> examples, boolean normalize, AbstractTrainingNode.LeafDeterminer leafDeterminer) Constructor which creates the inverted file.
RegressorTrainingNode(RegressorImpurity impurity, RegressorTrainingNode.InvertedData tuple, int dimIndex, String dimName, int numExamples, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputInfo, AbstractTrainingNode.LeafDeterminer leafDeterminer)

Uses of Regressor in org.tribuo.regression.sgd.linear

Methods in org.tribuo.regression.sgd.linear that return types with arguments of type Regressor

Modifier and Type	Method and Description
Prediction<Regressor>	LinearSGDModel.predict(Example<Regressor> example)

Methods in org.tribuo.regression.sgd.linear with parameters of type Regressor

Modifier and Type	Method and Description
protected DenseVector	LinearSGDTrainer.getTarget(ImmutableOutputInfo<Regressor> outputInfo, Regressor output)

Method parameters in org.tribuo.regression.sgd.linear with type arguments of type Regressor

Modifier and Type	Method and Description
protected LinearSGDModel	LinearSGDTrainer.createModel(String name, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo, LinearParameters parameters)
protected DenseVector	LinearSGDTrainer.getTarget(ImmutableOutputInfo<Regressor> outputInfo, Regressor output)
Prediction<Regressor>	LinearSGDModel.predict(Example<Regressor> example)

Uses of Regressor in org.tribuo.regression.slm

Methods in org.tribuo.regression.slm that return types with arguments of type Regressor

Modifier and Type	Method and Description
protected Model<Regressor>	SparseLinearModel.copy(String newName, ModelProvenance newProvenance)
Optional<Excuse<Regressor>>	SparseLinearModel.getExcuse(Example<Regressor> example)
SparseModel<Regressor>	ElasticNetCDTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Method parameters in org.tribuo.regression.slm with type arguments of type Regressor

Modifier and Type	Method and Description
protected SparseVector	SparseLinearModel.createFeatures(Example<Regressor> example) Creates the feature vector.
Optional<Excuse<Regressor>>	SparseLinearModel.getExcuse(Example<Regressor> example)
SparseLinearModel	SLMTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sparse linear model.
SparseModel<Regressor>	ElasticNetCDTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Regressor in org.tribuo.regression.xgboost

Methods in org.tribuo.regression.xgboost that return types with arguments of type Regressor

Modifier and Type	Method and Description
List<Prediction<Regressor>>	XGBoostRegressionConverter.convertBatchOutput(ImmutableOutputInfo<Regressor> info, List<float[][]> probabilities, int[] numValidFeatures, Example<Regressor>[] examples)
Prediction<Regressor>	XGBoostRegressionConverter.convertOutput(ImmutableOutputInfo<Regressor> info, List<float[]> probabilities, int numValidFeatures, Example<Regressor> example)
XGBoostModel<Regressor>	XGBoostRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Method parameters in org.tribuo.regression.xgboost with type arguments of type Regressor

Modifier and Type	Method and Description
List<Prediction<Regressor>>	XGBoostRegressionConverter.convertBatchOutput(ImmutableOutputInfo<Regressor> info, List<float[][]> probabilities, int[] numValidFeatures, Example<Regressor>[] examples)
Prediction<Regressor>	XGBoostRegressionConverter.convertOutput(ImmutableOutputInfo<Regressor> info, List<float[]> probabilities, int numValidFeatures, Example<Regressor> example)
Prediction<Regressor>	XGBoostRegressionConverter.convertOutput(ImmutableOutputInfo<Regressor> info, List<float[]> probabilities, int numValidFeatures, Example<Regressor> example)
XGBoostModel<Regressor>	XGBoostRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)