| Package | Description |
|---|---|
| org.tribuo.classification.ensemble |
Provides majority vote ensemble combiners for classification
along with an implementation of multiclass Adaboost.
|
| 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.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.regression.ensemble |
Provides
EnsembleCombiner implementations
for working with multi-output regression problems. |
| Modifier and Type | Class and Description |
|---|---|
class |
FullyWeightedVotingCombiner
A combiner which performs a weighted or unweighted vote across the predicted labels.
|
class |
VotingCombiner
A combiner which performs a weighted or unweighted vote across the predicted labels.
|
| Constructor and Description |
|---|
KNNTrainer(int k,
KNNTrainer.Distance distance,
int numThreads,
EnsembleCombiner<T> combiner,
KNNModel.Backend backend)
Creates a K-NN trainer using the supplied parameters.
|
| Constructor and Description |
|---|
ExtraTreesTrainer(DecisionTreeTrainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers)
Constructs an ExtraTreesTrainer with the default seed
Trainer.DEFAULT_SEED. |
ExtraTreesTrainer(DecisionTreeTrainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers,
long seed)
Constructs an ExtraTreesTrainer with the supplied seed, trainer, combining function and number of members.
|
RandomForestTrainer(DecisionTreeTrainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers)
Constructs a RandomForestTrainer with the default seed
Trainer.DEFAULT_SEED. |
RandomForestTrainer(DecisionTreeTrainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers,
long seed)
Constructs a RandomForestTrainer with the supplied seed, trainer, combining function and number of members.
|
| Modifier and Type | Field and Description |
|---|---|
protected EnsembleCombiner<T> |
WeightedEnsembleModel.combiner |
protected EnsembleCombiner<T> |
BaggingTrainer.combiner |
| Modifier and Type | Method and Description |
|---|---|
static <T extends Output<T>> |
WeightedEnsembleModel.createEnsembleFromExistingModels(String name,
List<Model<T>> models,
EnsembleCombiner<T> combiner)
Creates an ensemble from existing models.
|
static <T extends Output<T>> |
WeightedEnsembleModel.createEnsembleFromExistingModels(String name,
List<Model<T>> models,
EnsembleCombiner<T> combiner,
float[] weights)
Creates an ensemble from existing models.
|
| Constructor and Description |
|---|
BaggingTrainer(Trainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers) |
BaggingTrainer(Trainer<T> trainer,
EnsembleCombiner<T> combiner,
int numMembers,
long seed) |
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. |
| Modifier and Type | Class and Description |
|---|---|
class |
AveragingCombiner
A combiner which performs a weighted or unweighted average of the predicted
regressors independently across the output dimensions.
|
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