Package | Description |
---|---|
org.tribuo |
Provides the core interfaces and classes for using Tribuo.
|
org.tribuo.anomaly.liblinear |
Provides an interface to LibLinear-java for anomaly detection problems.
|
org.tribuo.classification.baseline |
Provides simple baseline multiclass classifiers.
|
org.tribuo.classification.liblinear |
Provides an interface to LibLinear-java 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.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.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.interop |
This package contains the abstract implementation of an external model
trained by something outside of Tribuo.
|
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.regression.baseline |
Provides simple baseline regression predictors.
|
org.tribuo.regression.liblinear |
Provides an interface to liblinear 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 . |
Modifier and Type | Method and Description |
---|---|
abstract Optional<Excuse<T>> |
Model.getExcuse(Example<T> example)
Generates an excuse for an example.
|
Optional<List<Excuse<T>>> |
Model.getExcuses(Iterable<Example<T>> examples)
Generates an excuse for each example.
|
Modifier and Type | Method and Description |
---|---|
protected Excuse<Event> |
LibLinearAnomalyModel.innerGetExcuse(Example<Event> 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.
|
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<Label>> |
DummyClassifierModel.getExcuse(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
protected Excuse<Label> |
LibLinearClassificationModel.innerGetExcuse(Example<Label> 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.
|
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<Label>> |
MultinomialNaiveBayesModel.getExcuse(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<Label>> |
KernelSVMModel.getExcuse(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<ClusterID>> |
KMeansModel.getExcuse(Example<ClusterID> example) |
Modifier and Type | Method and Description |
---|---|
protected abstract Excuse<T> |
LibLinearModel.innerGetExcuse(Example<T> e,
double[][] featureWeights)
The call to getFeatureWeights in the public methods copies the
weights array so this inner method exists to save the copy in getExcuses.
|
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
LibLinearModel.getExcuse(Example<T> e)
This call is expensive as it copies out the weight matrix from the
LibLinear model.
|
Optional<List<Excuse<T>>> |
LibLinearModel.getExcuses(Iterable<Example<T>> examples) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
LibSVMModel.getExcuse(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
KNNModel.getExcuse(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
AbstractLinearSGDModel.getExcuse(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
TreeModel.getExcuse(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
XGBoostModel.getExcuse(Example<T> example) |
Modifier and Type | Class and Description |
---|---|
class |
EnsembleExcuse<T extends Output<T>>
An
Excuse which has a List of excuses for each of the ensemble members. |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
WeightedEnsembleModel.getExcuse(Example<T> example) |
abstract Optional<Excuse<T>> |
EnsembleModel.getExcuse(Example<T> example) |
List<Excuse<T>> |
EnsembleExcuse.getInnerExcuses()
The individual ensemble member's excuses.
|
Constructor and Description |
---|
EnsembleExcuse(Example<T> example,
Prediction<T> prediction,
Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> weights,
List<Excuse<T>> innerExcuses) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
ExternalModel.getExcuse(Example<T> example)
By default third party models don't return excuses.
|
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
TensorFlowModel.getExcuse(Example<T> example)
Deep learning models don't do excuses.
|
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<MultiLabel>> |
IndependentMultiLabelModel.getExcuse(Example<MultiLabel> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<Regressor>> |
DummyRegressionModel.getExcuse(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<Regressor>> |
IndependentRegressionTreeModel.getExcuse(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<Regressor>> |
SparseLinearModel.getExcuse(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Optional<Excuse<T>> |
TransformedModel.getExcuse(Example<T> example) |
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