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.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.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	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	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	Description
<U extends Output<U>> Model<U>	Model.castModel(Class<U> outputType)	Casts the model to the specified output type, assuming it is valid.
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 its provenance and name with the supplied values.
static Model<?>	Model.deserialize(org.tribuo.protos.core.ModelProto proto)	Deserializes the model from the supplied protobuf.
static Model<?>	Model.deserializeFromFile(Path path)	Reads an instance of ModelProto from the supplied path and deserializes it.
static Model<?>	Model.deserializeFromStream(InputStream is)	Reads an instance of ModelProto from the supplied input stream and deserializes it.
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.
default Model<T>	Trainer.train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a predictive model using the examples in the given data set.

Constructors in org.tribuo with parameters of type Model

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

Uses of Model in org.tribuo.ensemble

Subclasses of Model in org.tribuo.ensemble

Modifier and Type	Class	Description
class	EnsembleModel<T extends Output<T>>	A model which contains a list of other Models.
final 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	Description
protected final List<Model<T>>	EnsembleModel.models	The list of models in this ensemble.

Methods in org.tribuo.ensemble that return Model

Modifier and Type	Method	Description
protected Model<T>	EnsembleModel.copy(String name, ModelProvenance newProvenance)
protected Model<T>	BaggingTrainer.trainSingleModel(Dataset<T> examples, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> labelIDs, int randInt, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a single model.

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

Modifier and Type	Method	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	Description
protected abstract EnsembleModel<T>	EnsembleModel.copy(String name, EnsembleModelProvenance newProvenance, List<Model<T>> newModels)	Copies this ensemble model.
protected EnsembleModel<T>	WeightedEnsembleModel.copy(String name, EnsembleModelProvenance newProvenance, List<Model<T>> newModels)
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

Modifier	Constructor	Description
protected	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	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	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.
final 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.
final 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.
final 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.
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.
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	Evaluator.evaluate(Model<T> model, Dataset<T> dataset)	Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
E	Evaluator.evaluate(Model<T> model, DataSource<T> datasource)	Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
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.
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>, 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(EvaluationMetric<T,C> metric, Model<T> model, List<? extends Dataset<T>> datasets)	Summarize a model's performance w.r.t.

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

Modifier and Type	Method	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>, 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.
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.summarizeCrossValidation(List<com.oracle.labs.mlrg.olcut.util.Pair<R,Model<T>>> evaluations)	Summarize all fields of a list of evaluations produced by CrossValidation.

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

Modifier	Constructor	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	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	Description
C	EvaluationMetric.createContext(Model<T> model, List<Prediction<T>> predictions)	Creates the context this metric uses to compute it's value.
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.

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

Modifier	Constructor	Description
protected	MetricContext(Model<T> model, List<Prediction<T>> predictions)	Constructs a metric context.

Uses of Model in org.tribuo.hash

Methods in org.tribuo.hash that return Model

Modifier and Type	Method	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.
Model<T>	HashingTrainer.train(Dataset<T> dataset, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)

Uses of Model in org.tribuo.transform

Subclasses of Model in org.tribuo.transform

Modifier and Type	Class	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.

Methods in org.tribuo.transform that return Model

Modifier and Type	Method	Description
Model<T>	TransformedModel.getInnerModel()	Gets the inner model to allow access to any class specific methods that model contains (e.g., to examine cluster centroids).