org.tribuo

Interface SparseTrainer<T extends Output<T>>

All Superinterfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<T>

All Known Subinterfaces:

DecisionTreeTrainer<T>

All Known Implementing Classes:

AbstractCARTTrainer, CARTClassificationTrainer, CARTJointRegressionTrainer, CARTRegressionTrainer, ElasticNetCDTrainer, LARSLassoTrainer, LARSTrainer, SkeletalIndependentRegressionSparseTrainer, SLMTrainer

public interface SparseTrainer<T extends Output<T>>
extends Trainer<T>

Denotes this trainer emits a SparseModel.

Field Summary

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Method Summary

Modifier and Type	Method and Description
default SparseModel<T>	train(Dataset<T> examples) Trains a sparse predictive model using the examples in the given data set.
SparseModel<T>	train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sparse predictive model using the examples in the given data set.

Methods inherited from interface org.tribuo.Trainer

getInvocationCount

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable

getProvenance

Method Detail

train

default SparseModel<T> train(Dataset<T> examples)

Trains a sparse predictive model using the examples in the given data set.

Specified by:

train in interface Trainer<T extends Output<T>>

Parameters:

examples - The data set containing the examples.

Returns:

A sparse predictive model that can be used to generate predictions for new examples.

train

SparseModel<T> train(Dataset<T> examples,
                     Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Trains a sparse predictive model using the examples in the given data set.

Specified by:

train in interface Trainer<T extends Output<T>>

Parameters:

examples - the data set containing the examples.

runProvenance - Training run specific provenance (e.g., fold number).

Returns:

a predictive model that can be used to generate predictions for new examples.