Uses of Class
org.tribuo.Model

Packages that use Model

Package	Description
org.tribuo	Provides the core interfaces and classes for using Tribuo.
org.tribuo.anomaly.evaluation	Evaluation classes for anomaly detection.
org.tribuo.anomaly.liblinear	Provides an interface to LibLinear-java for anomaly detection problems.
org.tribuo.anomaly.libsvm	Provides an interface to LibSVM for anomaly detection problems.
org.tribuo.classification	Provides classes and infrastructure for multiclass classification problems.
org.tribuo.classification.baseline	Provides simple baseline multiclass classifiers.
org.tribuo.classification.ensemble	Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
org.tribuo.classification.evaluation	Evaluation classes for multi-class classification.
org.tribuo.classification.experiments	Provides a set of main methods for interacting with classification tasks.
org.tribuo.classification.explanations	Provides core infrastructure for local model based explanations.
org.tribuo.classification.explanations.lime	Provides an implementation of LIME (Locally Interpretable Model Explanations).
org.tribuo.classification.liblinear	Provides an interface to LibLinear-java for classification problems.
org.tribuo.classification.libsvm	Provides an interface to LibSVM for classification problems.
org.tribuo.classification.mnb	Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
org.tribuo.classification.sgd.kernel	Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
org.tribuo.classification.sgd.linear	Provides an implementation of a classification linear model using Stochastic Gradient Descent.
org.tribuo.clustering.evaluation	Evaluation classes for clustering.
org.tribuo.clustering.kmeans	Provides a multithreaded implementation of K-Means, with a configurable distance function.
org.tribuo.common.liblinear	Provides base classes for using liblinear from Tribuo.
org.tribuo.common.libsvm	The base interface to LibSVM.
org.tribuo.common.nearest	Provides a K-Nearest Neighbours implementation which works across all Tribuo Output types.
org.tribuo.common.sgd	Provides the base classes for models trained with stochastic gradient descent.
org.tribuo.common.tree	Provides common functionality for building decision trees, irrespective of the predicted Output.
org.tribuo.common.xgboost	Provides abstract classes for interfacing with XGBoost abstracting away all the Output dependent parts.
org.tribuo.data	Provides classes for loading in data from disk, processing it into examples, and splitting datasets for things like cross-validation and train-test splits.
org.tribuo.data.columnar	Provides classes for processing columnar data and generating Examples.
org.tribuo.ensemble	Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
org.tribuo.evaluation	Evaluation base classes, along with code for train/test splits and cross validation.
org.tribuo.evaluation.metrics	This package contains the infrastructure classes for building evaluation metrics.
org.tribuo.hash	Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
org.tribuo.interop	This package contains the abstract implementation of an external model trained by something outside of Tribuo.
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.multilabel.baseline	Provides an implementation of independent multi-label classification that wraps a Label Trainer and uses it to make independent predictions of each label.
org.tribuo.multilabel.evaluation	Evaluation classes for multi-label classification using MultiLabel.
org.tribuo.regression.baseline	Provides simple baseline regression predictors.
org.tribuo.regression.evaluation	Evaluation classes for single or multi-dimensional regression.
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.slm	Provides implementations of sparse linear regression using various forms of regularisation penalty.
org.tribuo.transform	Provides infrastructure for applying transformations to a Dataset.

Uses of Model in org.tribuo

Classes in org.tribuo with type parameters of type Model

Modifier and Type	Interface and Description
interface	IncrementalTrainer<T extends Output<T>,U extends Model<T>> An interface for incremental training of Models.

Subclasses of Model in org.tribuo

Modifier and Type	Class and Description
class	SparseModel<T extends Output<T>> A model which uses a subset of the features it knows about to make predictions.

Methods in org.tribuo that return Model

Modifier and Type	Method and Description
Model<T>	Model.copy() Copies a model, returning a deep copy of any mutable state, and a shallow copy otherwise.
protected abstract Model<T>	Model.copy(String newName, ModelProvenance newProvenance) Copies a model, replacing it's provenance and name with the supplied values.
default Model<T>	Trainer.train(Dataset<T> examples) Trains a predictive model using the examples in the given data set.
Model<T>	Trainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a predictive model using the examples in the given data set.

Constructors in org.tribuo with parameters of type Model

Constructor and Description
ImmutableDataset(DataSource<T> dataSource, Model<T> model, boolean dropInvalidExamples) Creates a dataset from a data source.

Uses of Model in org.tribuo.anomaly.evaluation

Methods in org.tribuo.anomaly.evaluation with parameters of type Model

Modifier and Type	Method and Description
org.tribuo.anomaly.evaluation.AnomalyMetric.Context	AnomalyMetric.createContext(Model<Event> model, List<Prediction<Event>> predictions)
protected org.tribuo.anomaly.evaluation.AnomalyMetric.Context	AnomalyEvaluator.createContext(Model<Event> model, List<Prediction<Event>> predictions)
protected Set<AnomalyMetric>	AnomalyEvaluator.createMetrics(Model<Event> model)

Uses of Model in org.tribuo.anomaly.liblinear

Subclasses of Model in org.tribuo.anomaly.liblinear

Modifier and Type	Class and Description
class	LibLinearAnomalyModel A Model which wraps a LibLinear-java anomaly detection model.

Uses of Model in org.tribuo.anomaly.libsvm

Subclasses of Model in org.tribuo.anomaly.libsvm

Modifier and Type	Class and Description
class	LibSVMAnomalyModel A anomaly detection model that uses an underlying libSVM model to make the predictions.

Uses of Model in org.tribuo.classification

Methods in org.tribuo.classification that return Model

Modifier and Type	Method and Description
static Model<Label>	TrainTestHelper.run(com.oracle.labs.mlrg.olcut.config.ConfigurationManager cm, DataOptions dataOptions, Trainer<Label> trainer) This method trains a model on the specified training data, and evaluates it on the specified test data.

Uses of Model in org.tribuo.classification.baseline

Subclasses of Model in org.tribuo.classification.baseline

Modifier and Type	Class and Description
class	DummyClassifierModel A model which performs dummy classifications (e.g., constant output, uniform sampled labels, stratified sampled labels).

Methods in org.tribuo.classification.baseline that return Model

Modifier and Type	Method and Description
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Uses of Model in org.tribuo.classification.ensemble

Methods in org.tribuo.classification.ensemble that return Model

Modifier and Type	Method and Description
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.

Uses of Model in org.tribuo.classification.evaluation

Methods in org.tribuo.classification.evaluation with parameters of type Model

Modifier and Type	Method and Description
LabelMetric.Context	LabelMetric.createContext(Model<Label> model, List<Prediction<Label>> predictions)
protected LabelMetric.Context	LabelEvaluator.createContext(Model<Label> model, List<Prediction<Label>> predictions)
protected Set<LabelMetric>	LabelEvaluator.createMetrics(Model<Label> model)

Constructors in org.tribuo.classification.evaluation with parameters of type Model

Constructor and Description
Context(Model<Label> model, List<Prediction<Label>> predictions)
LabelConfusionMatrix(Model<Label> model, List<Prediction<Label>> predictions) Creates a confusion matrix from the supplied predictions, using the label info from the supplied model.

Uses of Model in org.tribuo.classification.experiments

Methods in org.tribuo.classification.experiments that return types with arguments of type Model

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<Model<Label>,Dataset<Label>>	Test.load(Test.ConfigurableTestOptions o)

Uses of Model in org.tribuo.classification.explanations

Methods in org.tribuo.classification.explanations that return Model

Modifier and Type	Method and Description
Model<T>	Explanation.getModel() Returns the explanining model.

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

Fields in org.tribuo.classification.explanations.lime declared as Model

Modifier and Type	Field and Description
protected Model<Label>	LIMEBase.innerModel

Constructors in org.tribuo.classification.explanations.lime with parameters of type Model

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.
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 Model in org.tribuo.classification.liblinear

Subclasses of Model in org.tribuo.classification.liblinear

Modifier and Type	Class and Description
class	LibLinearClassificationModel A Model which wraps a LibLinear-java classification model.

Uses of Model in org.tribuo.classification.libsvm

Subclasses of Model in org.tribuo.classification.libsvm

Modifier and Type	Class and Description
class	LibSVMClassificationModel A classification model that uses an underlying LibSVM model to make the predictions.

Uses of Model in org.tribuo.classification.mnb

Subclasses of Model in org.tribuo.classification.mnb

Modifier and Type	Class and Description
class	MultinomialNaiveBayesModel A Model for multinomial Naive Bayes with Laplace smoothing.

Methods in org.tribuo.classification.mnb that return Model

Modifier and Type	Method and Description
Model<Label>	MultinomialNaiveBayesTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Model in org.tribuo.classification.sgd.kernel

Subclasses of Model in org.tribuo.classification.sgd.kernel

Modifier and Type	Class and Description
class	KernelSVMModel The inference time version of a kernel model trained using Pegasos.

Uses of Model in org.tribuo.classification.sgd.linear

Subclasses of Model in org.tribuo.classification.sgd.linear

Modifier and Type	Class and Description
class	LinearSGDModel The inference time version of a linear model trained using SGD.

Uses of Model in org.tribuo.clustering.evaluation

Methods in org.tribuo.clustering.evaluation with parameters of type Model

Modifier and Type	Method and Description
org.tribuo.clustering.evaluation.ClusteringMetric.Context	ClusteringMetric.createContext(Model<ClusterID> model, List<Prediction<ClusterID>> predictions)
protected org.tribuo.clustering.evaluation.ClusteringMetric.Context	ClusteringEvaluator.createContext(Model<ClusterID> model, List<Prediction<ClusterID>> predictions)
protected Set<ClusteringMetric>	ClusteringEvaluator.createMetrics(Model<ClusterID> model)

Uses of Model in org.tribuo.clustering.kmeans

Subclasses of Model in org.tribuo.clustering.kmeans

Modifier and Type	Class and Description
class	KMeansModel A K-Means model with a selectable distance function.

Uses of Model in org.tribuo.common.liblinear

Subclasses of Model in org.tribuo.common.liblinear

Modifier and Type	Class and Description
class	LibLinearModel<T extends Output<T>> A Model which wraps a LibLinear-java model.

Uses of Model in org.tribuo.common.libsvm

Subclasses of Model in org.tribuo.common.libsvm

Modifier and Type	Class and Description
class	LibSVMModel<T extends Output<T>> A model that uses an underlying libSVM model to make the predictions.

Uses of Model in org.tribuo.common.nearest

Subclasses of Model in org.tribuo.common.nearest

Modifier and Type	Class and Description
class	KNNModel<T extends Output<T>> A k-nearest neighbours model.

Methods in org.tribuo.common.nearest that return Model

Modifier and Type	Method and Description
Model<T>	KNNTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Model in org.tribuo.common.sgd

Classes in org.tribuo.common.sgd with type parameters of type Model

Modifier and Type	Class and Description
class	AbstractSGDTrainer<T extends Output<T>,U,V extends Model<T>,X extends FeedForwardParameters> A trainer for a model which uses SGD.

Subclasses of Model in org.tribuo.common.sgd

Modifier and Type	Class and Description
class	AbstractLinearSGDModel<T extends Output<T>>
class	AbstractSGDModel<T extends Output<T>>

Uses of Model in org.tribuo.common.tree

Subclasses of Model in org.tribuo.common.tree

Modifier and Type	Class and Description
class	TreeModel<T extends Output<T>> A Model wrapped around a decision tree root Node.

Uses of Model in org.tribuo.common.xgboost

Subclasses of Model in org.tribuo.common.xgboost

Modifier and Type	Class and Description
class	XGBoostExternalModel<T extends Output<T>> A Model which wraps around a XGBoost.Booster which was trained by a system other than Tribuo.
class	XGBoostModel<T extends Output<T>> A Model which wraps around a XGBoost.Booster.

Methods in org.tribuo.common.xgboost that return Model

Modifier and Type	Method and Description
protected Model<T>	XGBoostModel.copy(String newName, ModelProvenance newProvenance)

Uses of Model in org.tribuo.data

Methods in org.tribuo.data with parameters of type Model

Modifier and Type	Method and Description
<T extends Output<T>> void	DataOptions.saveModel(Model<T> model)

Uses of Model in org.tribuo.data.columnar

Methods in org.tribuo.data.columnar with parameters of type Model

Modifier and Type	Method and Description
void	RowProcessor.expandRegexMapping(Model<T> model) Uses similar logic to TransformationMap.validateTransformations(org.tribuo.FeatureMap) to check the regexes against the ImmutableFeatureMap contained in the supplied Model.

Uses of Model in org.tribuo.ensemble

Subclasses of Model in org.tribuo.ensemble

Modifier and Type	Class and Description
class	EnsembleModel<T extends Output<T>> A model which contains a list of other Models.
class	WeightedEnsembleModel<T extends Output<T>> An ensemble model that uses weights to combine the ensemble member predictions.

Fields in org.tribuo.ensemble with type parameters of type Model

Modifier and Type	Field and Description
protected List<Model<T>>	EnsembleModel.models

Methods in org.tribuo.ensemble that return Model

Modifier and Type	Method and Description
protected Model<T>	EnsembleModel.copy(String name, ModelProvenance newProvenance)
Model<T>	BaggingTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
protected Model<T>	BaggingTrainer.trainSingleModel(Dataset<T> examples, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> labelIDs, SplittableRandom localRNG, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

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

Modifier and Type	Method and Description
List<Model<T>>	EnsembleModel.getModels() Returns an unmodifiable view on the ensemble members.

Method parameters in org.tribuo.ensemble with type arguments of type Model

Modifier and Type	Method and Description
protected EnsembleModel<T>	WeightedEnsembleModel.copy(String name, EnsembleModelProvenance newProvenance, List<Model<T>> newModels)
protected abstract EnsembleModel<T>	EnsembleModel.copy(String name, EnsembleModelProvenance newProvenance, List<Model<T>> newModels) Copies this ensemble model.
static <T extends Output<T>> WeightedEnsembleModel<T>	WeightedEnsembleModel.createEnsembleFromExistingModels(String name, List<Model<T>> models, EnsembleCombiner<T> combiner) Creates an ensemble from existing models.
static <T extends Output<T>> WeightedEnsembleModel<T>	WeightedEnsembleModel.createEnsembleFromExistingModels(String name, List<Model<T>> models, EnsembleCombiner<T> combiner, float[] weights) Creates an ensemble from existing models.

Constructor parameters in org.tribuo.ensemble with type arguments of type Model

Constructor and Description
EnsembleModel(String name, EnsembleModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, List<Model<T>> newModels) Builds an EnsembleModel from the supplied model list.
WeightedEnsembleModel(String name, EnsembleModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, List<Model<T>> newModels, EnsembleCombiner<T> combiner) Unless you are implementing a Trainer you should not use this constructor directly.
WeightedEnsembleModel(String name, EnsembleModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, List<Model<T>> newModels, EnsembleCombiner<T> combiner, float[] weights) Unless you are implementing a Trainer you should not use this constructor directly.

Uses of Model in org.tribuo.evaluation

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

Modifier and Type	Method and Description
List<com.oracle.labs.mlrg.olcut.util.Pair<E,Model<T>>>	CrossValidation.evaluate() Performs k fold cross validation, returning the k evaluations.

Methods in org.tribuo.evaluation with parameters of type Model

Modifier and Type	Method and Description
static <T extends Output<T>,C extends MetricContext<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,Double>	EvaluationAggregator.argmax(EvaluationMetric<T,C> metric, Model<T> model, List<? extends Dataset<T>> datasets) Calculates the argmax of a metric across the supplied datasets.
protected abstract C	AbstractEvaluator.createContext(Model<T> model, List<Prediction<T>> predictions) Create the context needed for evaluation.
protected abstract Set<M>	AbstractEvaluator.createMetrics(Model<T> model) Creates the appropriate set of metrics for this model, by querying for it's OutputInfo.
default OnlineEvaluator<T,E>	Evaluator.createOnlineEvaluator(Model<T> model, DataProvenance provenance) Creates an online evaluator that maintains a list of all the predictions it has seen and can evaluate them upon request.
E	Evaluator.evaluate(Model<T> model, Dataset<T> dataset) Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
E	AbstractEvaluator.evaluate(Model<T> model, Dataset<T> dataset) Produces an evaluation for the supplied model and dataset, by calling predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
E	Evaluator.evaluate(Model<T> model, DataSource<T> datasource) Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
E	AbstractEvaluator.evaluate(Model<T> model, DataSource<T> datasource) Produces an evaluation for the supplied model and datasource, by calling predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
E	Evaluator.evaluate(Model<T> model, List<Prediction<T>> predictions, DataProvenance dataProvenance) Evaluates the model performance using the supplied predictions, returning an immutable Evaluation of the appropriate type.
E	AbstractEvaluator.evaluate(Model<T> model, List<Prediction<T>> predictions, DataProvenance dataProvenance) Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
default E	Evaluator.evaluate(Model<T> model, List<Prediction<T>> predictions, List<T> groundTruth, DataProvenance dataProvenance) Evaluates the model performance using the supplied predictions, returning an immutable Evaluation of the appropriate type.
static <T extends Output<T>,C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(EvaluationMetric<T,C> metric, Model<T> model, List<? extends Dataset<T>> datasets) Summarize a model's performance w.r.t.
static <T extends Output<T>,R extends Evaluation<T>> Map<MetricID<T>,DescriptiveStats>	EvaluationAggregator.summarize(Evaluator<T,R> evaluator, Model<T> model, List<? extends Dataset<T>> datasets) Summarize performance according to evaluator for a single model across several datasets.
static <T extends Output<T>,C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(List<? extends EvaluationMetric<T,C>> metrics, Model<T> model, Dataset<T> dataset) Summarize model performance on dataset across several metrics.
static <T extends Output<T>,C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(List<? extends EvaluationMetric<T,C>> metrics, Model<T> model, List<Prediction<T>> predictions) Summarize model performance on dataset across several metrics.

Method parameters in org.tribuo.evaluation with type arguments of type Model

Modifier and Type	Method and Description
static <T extends Output<T>,C extends MetricContext<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,Double>	EvaluationAggregator.argmax(EvaluationMetric<T,C> metric, List<? extends Model<T>> models, Dataset<T> dataset) Calculates the argmax of a metric across the supplied models (i.e., the index of the model which performed the best).
static <T extends Output<T>,C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(EvaluationMetric<T,C> metric, List<? extends Model<T>> models, Dataset<T> dataset) Summarize performance w.r.t.
static <T extends Output<T>,R extends Evaluation<T>> Map<MetricID<T>,DescriptiveStats>	EvaluationAggregator.summarize(Evaluator<T,R> evaluator, List<? extends Model<T>> models, Dataset<T> dataset) Summarize performance using the supplied evaluator across several models on one dataset.

Constructors in org.tribuo.evaluation with parameters of type Model

Constructor and Description
OnlineEvaluator(Evaluator<T,E> evaluator, Model<T> model, DataProvenance provenance) Constructs an OnlineEvaluator which accumulates predictions.

Uses of Model in org.tribuo.evaluation.metrics

Methods in org.tribuo.evaluation.metrics that return Model

Modifier and Type	Method and Description
Model<T>	MetricContext.getModel() Gets the Model used by this context.

Methods in org.tribuo.evaluation.metrics with parameters of type Model

Modifier and Type	Method and Description
default C	EvaluationMetric.createContext(Model<T> model, Dataset<T> dataset) Creates the metric context used to compute this metric's value, generating Predictions for each Example in the supplied dataset.
C	EvaluationMetric.createContext(Model<T> model, List<Prediction<T>> predictions) Creates the context this metric uses to compute it's value.

Constructors in org.tribuo.evaluation.metrics with parameters of type Model

Constructor and Description
MetricContext(Model<T> model, List<Prediction<T>> predictions)

Uses of Model in org.tribuo.hash

Methods in org.tribuo.hash that return Model

Modifier and Type	Method and Description
Model<T>	HashingTrainer.train(Dataset<T> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance) This clones the Dataset, hashes each of the examples and rewrites their feature ids before passing it to the inner trainer.

Uses of Model in org.tribuo.interop

Subclasses of Model in org.tribuo.interop

Modifier and Type	Class and Description
class	ExternalModel<T extends Output<T>,U,V> This is the base class for third party models which are trained externally and loaded into Tribuo for prediction.

Uses of Model in org.tribuo.interop.onnx

Subclasses of Model in org.tribuo.interop.onnx

Modifier and Type	Class and Description
class	ONNXExternalModel<T extends Output<T>> A Tribuo wrapper around a ONNX model.

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

Modifier and Type	Method and Description
protected Model<T>	ONNXExternalModel.copy(String newName, ModelProvenance newProvenance)

Uses of Model in org.tribuo.interop.tensorflow

Subclasses of Model in org.tribuo.interop.tensorflow

Modifier and Type	Class and Description
class	TensorFlowCheckpointModel<T extends Output<T>> This model encapsulates a simple model with an input feed dict, and produces a single output tensor.
class	TensorFlowFrozenExternalModel<T extends Output<T>> A Tribuo wrapper around a TensorFlow frozen model.
class	TensorFlowModel<T extends Output<T>> Base class for a TensorFlow model that operates on Examples.
class	TensorFlowNativeModel<T extends Output<T>> This model encapsulates a TensorFlow model running in graph mode with a single tensor output.
class	TensorFlowSavedModelExternalModel<T extends Output<T>> A Tribuo wrapper around a TensorFlow saved model bundle.

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

Modifier and Type	Method and Description
protected Model<T>	TensorFlowSavedModelExternalModel.copy(String newName, ModelProvenance newProvenance)
protected Model<T>	TensorFlowFrozenExternalModel.copy(String newName, ModelProvenance newProvenance)

Uses of Model in org.tribuo.multilabel.baseline

Subclasses of Model in org.tribuo.multilabel.baseline

Modifier and Type	Class and Description
class	IndependentMultiLabelModel A Model which wraps n binary models, where n is the size of the MultiLabel domain.

Methods in org.tribuo.multilabel.baseline that return Model

Modifier and Type	Method and Description
Model<MultiLabel>	IndependentMultiLabelTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Model in org.tribuo.multilabel.evaluation

Methods in org.tribuo.multilabel.evaluation with parameters of type Model

Modifier and Type	Method and Description
org.tribuo.multilabel.evaluation.MultiLabelMetric.Context	MultiLabelMetric.createContext(Model<MultiLabel> model, List<Prediction<MultiLabel>> predictions)
protected org.tribuo.multilabel.evaluation.MultiLabelMetric.Context	MultiLabelEvaluator.createContext(Model<MultiLabel> model, List<Prediction<MultiLabel>> predictions)
protected Set<MultiLabelMetric>	MultiLabelEvaluator.createMetrics(Model<MultiLabel> model)

Constructors in org.tribuo.multilabel.evaluation with parameters of type Model

Constructor and Description
MultiLabelConfusionMatrix(Model<MultiLabel> model, List<Prediction<MultiLabel>> predictions)

Uses of Model in org.tribuo.regression.baseline

Subclasses of Model in org.tribuo.regression.baseline

Modifier and Type	Class and Description
class	DummyRegressionModel A model which performs dummy regressions (e.g., constant output, gaussian sampled output, mean value, median, quartile).

Methods in org.tribuo.regression.baseline that return Model

Modifier and Type	Method and Description
protected Model<Regressor>	DummyRegressionModel.copy(String newName, ModelProvenance newProvenance)

Uses of Model in org.tribuo.regression.evaluation

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

Modifier and Type	Method and Description
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 Set<RegressionMetric>	RegressionEvaluator.createMetrics(Model<Regressor> model)

Uses of Model in org.tribuo.regression.impl

Subclasses of Model in org.tribuo.regression.impl

Modifier and Type	Class and Description
class	SkeletalIndependentRegressionModel A Model which wraps n independent regression models, where n is the size of the MultipleRegressor domain.
class	SkeletalIndependentRegressionSparseModel A SparseModel which wraps n independent regression models, where n is the size of the MultipleRegressor domain.

Uses of Model in org.tribuo.regression.liblinear

Subclasses of Model in org.tribuo.regression.liblinear

Modifier and Type	Class and Description
class	LibLinearRegressionModel A Model which wraps a LibLinear-java model.

Uses of Model in org.tribuo.regression.libsvm

Subclasses of Model in org.tribuo.regression.libsvm

Modifier and Type	Class and Description
class	LibSVMRegressionModel A regression model that uses an underlying libSVM model to make the predictions.

Uses of Model in org.tribuo.regression.rtree

Subclasses of Model in org.tribuo.regression.rtree

Modifier and Type	Class and Description
class	IndependentRegressionTreeModel A Model wrapped around a list of decision tree root Nodes used to generate independent predictions for each dimension in a regression.

Uses of Model in org.tribuo.regression.slm

Subclasses of Model in org.tribuo.regression.slm

Modifier and Type	Class and Description
class	SparseLinearModel The inference time version of a sparse linear regression model.

Methods in org.tribuo.regression.slm that return Model

Modifier and Type	Method and Description
protected Model<Regressor>	SparseLinearModel.copy(String newName, ModelProvenance newProvenance)

Uses of Model in org.tribuo.transform

Subclasses of Model in org.tribuo.transform

Modifier and Type	Class and Description
class	TransformedModel<T extends Output<T>> Wraps a Model with it's TransformerMap so all Examples are transformed appropriately before the model makes predictions.