| Package | Description |
|---|---|
| org.tribuo |
Provides the core interfaces and classes for using Tribuo.
|
| 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.dtree |
Provides implementations of decision trees for classification problems.
|
| org.tribuo.classification.ensemble |
Provides majority vote ensemble combiners for classification
along with an implementation of multiclass Adaboost.
|
| org.tribuo.classification.experiments |
Provides a set of main methods for interacting with classification tasks.
|
| 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.sequence.viterbi |
Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any
Label based classification model. |
| 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.classification.xgboost |
Provides an interface to XGBoost for classification problems.
|
| 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.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.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.hash |
Provides the base interface and implementations of the
Model hashing
which obscures the feature names stored in a model. |
| 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 | |
| org.tribuo.provenance |
Provides Tribuo specific infrastructure for the
Provenance system which
tracks models and datasets. |
| org.tribuo.provenance.impl |
Provides internal implementations for empty provenance classes and TrainerProvenance.
|
| org.tribuo.regression.baseline |
Provides simple baseline regression predictors.
|
| org.tribuo.regression.impl | |
| 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.regression.xgboost |
Provides an interface to XGBoost for regression problems.
|
| org.tribuo.transform |
Provides infrastructure for applying transformations to a
Dataset. |
| Modifier and Type | Interface and Description |
|---|---|
interface |
IncrementalTrainer<T extends Output<T>,U extends Model<T>>
An interface for incremental training of
Models. |
interface |
SparseTrainer<T extends Output<T>>
Denotes this trainer emits a
SparseModel. |
| Modifier and Type | Class and Description |
|---|---|
class |
LibSVMAnomalyTrainer
A trainer for anomaly models that uses LibSVM.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ClassificationOptions<TRAINER extends Trainer<Label>>
An
Options that can produce a classification Trainer based on the
provided arguments. |
| 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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DummyClassifierTrainer
A trainer for simple baseline classifiers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CARTClassificationTrainer
A
Trainer that uses an approximation of the CART algorithm to build a decision tree. |
| Modifier and Type | Class and Description |
|---|---|
class |
AdaBoostTrainer
Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.
|
| Modifier and Type | Field and Description |
|---|---|
protected Trainer<Label> |
AdaBoostTrainer.innerTrainer |
| Modifier and Type | Method and Description |
|---|---|
Trainer<Label> |
ClassificationEnsembleOptions.wrapTrainer(Trainer<Label> trainer) |
| Modifier and Type | Method and Description |
|---|---|
Trainer<Label> |
ClassificationEnsembleOptions.wrapTrainer(Trainer<Label> trainer) |
| Constructor and Description |
|---|
AdaBoostTrainer(Trainer<Label> trainer,
int numMembers)
Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of
boosting rounds.
|
AdaBoostTrainer(Trainer<Label> trainer,
int numMembers,
long seed)
Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of
boosting rounds and the supplied seed.
|
| Modifier and Type | Field and Description |
|---|---|
Trainer<Label> |
ConfigurableTrainTest.ConfigurableTrainTestOptions.trainer |
| Modifier and Type | Method and Description |
|---|---|
Trainer<Label> |
AllTrainerOptions.getTrainer() |
| Modifier and Type | Class and Description |
|---|---|
class |
LibLinearClassificationTrainer
A
Trainer which wraps a liblinear-java classifier trainer. |
| Modifier and Type | Class and Description |
|---|---|
class |
LibSVMClassificationTrainer
A trainer for classification models that uses LibSVM.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultinomialNaiveBayesTrainer
A
Trainer which trains a multinomial Naive Bayes model with Laplace smoothing. |
| Modifier and Type | Method and Description |
|---|---|
ViterbiTrainer |
ViterbiTrainerOptions.getSequenceTrainer(Trainer<Label> innerTrainer) |
| Constructor and Description |
|---|
ViterbiTrainer(Trainer<Label> trainer,
LabelFeatureExtractor labelFeatureExtractor,
int stackSize,
ViterbiModel.ScoreAggregation scoreAggregation) |
ViterbiTrainer(Trainer<Label> trainer,
LabelFeatureExtractor labelFeatureExtractor,
ViterbiModel.ScoreAggregation scoreAggregation) |
| Modifier and Type | Class and Description |
|---|---|
class |
KernelSVMTrainer
A trainer for a kernelised model using the Pegasos optimiser.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinearSGDTrainer
A trainer for a linear model which uses SGD.
|
class |
LogisticRegressionTrainer
A logistic regression trainer that uses a reasonable objective, optimiser,
number of epochs and minibatch size.
|
| Modifier and Type | Class and Description |
|---|---|
class |
XGBoostClassificationTrainer
A
Trainer which wraps the XGBoost training procedure. |
| Modifier and Type | Class and Description |
|---|---|
class |
KMeansTrainer
A K-Means trainer, which generates a K-means clustering of the supplied
data.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LibLinearTrainer<T extends Output<T>>
A
Trainer which wraps a liblinear-java trainer. |
| Modifier and Type | Class and Description |
|---|---|
class |
LibSVMTrainer<T extends Output<T>>
A trainer that will train using libsvm's Java implementation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KNNTrainer<T extends Output<T>>
A
Trainer for k-nearest neighbour models. |
| Modifier and Type | Interface and Description |
|---|---|
interface |
DecisionTreeTrainer<T extends Output<T>>
A tag interface for a
Trainer so the random forests trainer can check if it's actually a tree. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractCARTTrainer<T extends Output<T>>
Base class for
Trainer's that use an approximation of the CART algorithm to build a decision tree. |
class |
RandomForestTrainer<T extends Output<T>>
A trainer which produces a random forest.
|
| Modifier and Type | Class and Description |
|---|---|
class |
XGBoostTrainer<T extends Output<T>>
A
Trainer which wraps the XGBoost training procedure. |
| Modifier and Type | Field and Description |
|---|---|
Trainer<?> |
ConfigurableTrainTest.ConfigurableTrainTestOptions.trainer |
Trainer<?> |
CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions.trainer |
| Modifier and Type | Class and Description |
|---|---|
class |
BaggingTrainer<T extends Output<T>>
A Trainer that wraps another trainer and produces a bagged ensemble.
|
| Modifier and Type | Field and Description |
|---|---|
protected Trainer<T> |
BaggingTrainer.innerTrainer |
| Constructor and Description |
|---|
BaggingTrainer(Trainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers) |
BaggingTrainer(Trainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers,
long seed) |
| Constructor and Description |
|---|
CrossValidation(Trainer<T> trainer,
Dataset<T> data,
Evaluator<T,E> evaluator,
int k)
Builds a k-fold cross-validation loop.
|
CrossValidation(Trainer<T> trainer,
Dataset<T> data,
Evaluator<T,E> evaluator,
int k,
long seed)
Builds a k-fold cross-validation loop.
|
| Modifier and Type | Class and Description |
|---|---|
class |
HashingTrainer<T extends Output<T>>
|
| Modifier and Type | Method and Description |
|---|---|
<T extends Output<T>> |
HashingOptions.getHashedTrainer(Trainer<T> innerTrainer)
Gets the trainer wrapped in a hashing trainer.
|
| Modifier and Type | Method and Description |
|---|---|
<T extends Output<T>> |
HashingOptions.getHashedTrainer(Trainer<T> innerTrainer)
Gets the trainer wrapped in a hashing trainer.
|
| Constructor and Description |
|---|
HashingTrainer(Trainer<T> trainer,
Hasher hasher) |
| Modifier and Type | Class and Description |
|---|---|
class |
TensorflowCheckpointTrainer<T extends Output<T>>
Trainer for Tensorflow.
|
class |
TensorflowTrainer<T extends Output<T>>
Trainer for Tensorflow.
|
| Modifier and Type | Class and Description |
|---|---|
class |
IndependentMultiLabelTrainer
|
| Constructor and Description |
|---|
IndependentMultiLabelTrainer(Trainer<Label> innerTrainer) |
| Constructor and Description |
|---|
SkeletalTrainerProvenance(Trainer<T> host) |
| Constructor and Description |
|---|
TrainerProvenanceImpl(Trainer<T> host)
Construct a TrainerProvenance by reading all the configurable parameters
along with the train call count.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DummyRegressionTrainer
A trainer for simple baseline regressors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SkeletalIndependentRegressionSparseTrainer<T>
Base class for training n independent sparse models, one per dimension.
|
class |
SkeletalIndependentRegressionTrainer<T>
|
| Modifier and Type | Class and Description |
|---|---|
class |
LibLinearRegressionTrainer
A
Trainer which wraps a liblinear-java regression trainer. |
| Modifier and Type | Class and Description |
|---|---|
class |
LibSVMRegressionTrainer
A trainer for regression models that uses LibSVM.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CARTJointRegressionTrainer
A
Trainer that uses an approximation of the CART algorithm to build a decision tree. |
class |
CARTRegressionTrainer
A
Trainer that uses an approximation of the CART algorithm to build a decision tree. |
| Modifier and Type | Class and Description |
|---|---|
class |
ElasticNetCDTrainer
An ElasticNet trainer that uses co-ordinate descent.
|
class |
LARSLassoTrainer
A trainer for a lasso linear regression model which uses LARS to construct the model.
|
class |
LARSTrainer
A trainer for a linear regression model which uses least angle regression.
|
class |
SLMTrainer
A trainer for a sparse linear regression model.
|
| Modifier and Type | Class and Description |
|---|---|
class |
XGBoostRegressionTrainer
A
Trainer which wraps the XGBoost training procedure. |
| Modifier and Type | Class and Description |
|---|---|
class |
TransformTrainer<T extends Output<T>>
A
Trainer which encapsulates another trainer plus a TransformationMap object
to apply to each Dataset before training each Model. |
| Constructor and Description |
|---|
TransformTrainer(Trainer<T> innerTrainer,
TransformationMap transformations)
Creates a trainer which transforms the data before training, and stores
the transformers along with the trained model in a
TransformedModel. |
TransformTrainer(Trainer<T> innerTrainer,
TransformationMap transformations,
boolean densify)
Creates a trainer which transforms the data before training, and stores
the transformers along with the trained model in a
TransformedModel. |
Copyright © 2015–2021 Oracle and/or its affiliates. All rights reserved.