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, INCREMENT_INVOCATION_COUNT

  • Method Summary

    Modifier and Type
    Method
    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.
    default SparseModel<T>
    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.

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

    postConfig

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

    getProvenance

    Methods inherited from interface org.tribuo.Trainer

    getInvocationCount, setInvocationCount

  • Method Details

    • 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.

    • train

      default SparseModel<T> 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.
      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).
      invocationCount - The state of the RNG the trainer should be set to before training
      Returns:
      a predictive model that can be used to generate predictions for new examples.