com.datumbox.framework.core.machinelearning.common.interfaces

Interface Trainable<MP extends Learnable,TP extends Parameterizable>

Type Parameters:

MP -

TP -

All Superinterfaces:

java.lang.AutoCloseable, Savable

All Known Implementing Classes:

AbstractBoostingBagging, AbstractClassifier, AbstractClusterer, AbstractCountBasedFeatureSelector, AbstractDPMM, AbstractEncoder, AbstractFeatureSelector, AbstractModeler, AbstractNaiveBayes, AbstractRecommender, AbstractRegressor, AbstractScaler, AbstractScoreBasedFeatureSelector, AbstractTopicModeler, AbstractTrainer, AbstractTransformer, Adaboost, BayesianEnsembleMethod, BernoulliNaiveBayes, BinarizedNaiveBayes, BinaryScaler, BootstrapAggregating, ChisquareSelect, CollaborativeFiltering, CornerConstraintsEncoder, GaussianDPMM, HierarchicalAgglomerative, Kmeans, LatentDirichletAllocation, MatrixLinearRegression, MaxAbsScaler, MaximumEntropy, MinMaxScaler, Modeler, MultinomialDPMM, MultinomialNaiveBayes, MutualInformation, NLMS, OneHotEncoder, OrdinalRegression, PCA, SoftMaxRegression, StandardScaler, StepwiseRegression, SupportVectorMachine, TextClassifier, TFIDF

public interface Trainable<MP extends Learnable,TP extends Parameterizable>
extends Savable

This interface is used to mark classes that can be trained. This interface used for classes that perform training/analysis and learn parameters.

Method Summary

Modifier and Type	Method and Description
void	fit(Dataframe trainingData) Trains a model using the provided training parameters and data.
MP	getModelParameters() Returns the model parameters that were estimated after training.
TP	getTrainingParameters() It returns the training parameters that configure the algorithm.

Methods inherited from interface com.datumbox.framework.core.common.interfaces.Savable

delete, save

Methods inherited from interface java.lang.AutoCloseable

close

Method Detail

getModelParameters

MP getModelParameters()

Returns the model parameters that were estimated after training.

Returns:

getTrainingParameters

TP getTrainingParameters()

It returns the training parameters that configure the algorithm.

Returns:

fit

void fit(Dataframe trainingData)

Trains a model using the provided training parameters and data.

Parameters:

trainingData -