Uses of Class
org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Packages that use LogisticRegressionModel

Package	Description
org.apache.ignite.ml.regressions.logistic	Contains various logistic regressions.

Uses of LogisticRegressionModel in org.apache.ignite.ml.regressions.logistic

Methods in org.apache.ignite.ml.regressions.logistic that return LogisticRegressionModel

Modifier and Type	Method and Description
<K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
protected <K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.
LogisticRegressionModel	LogisticRegressionModel.withIntercept(double intercept) Set up the intercept.
LogisticRegressionModel	LogisticRegressionModel.withRawLabels(boolean isKeepingRawLabels) Set up the output label format.
LogisticRegressionModel	LogisticRegressionModel.withThreshold(double threshold) Set up the threshold.
LogisticRegressionModel	LogisticRegressionModel.withWeights(Vector weights) Set up the weights.

Methods in org.apache.ignite.ml.regressions.logistic with parameters of type LogisticRegressionModel

Modifier and Type	Method and Description
boolean	LogisticRegressionSGDTrainer.isUpdateable(LogisticRegressionModel mdl)
protected <K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.

Method parameters in org.apache.ignite.ml.regressions.logistic with type arguments of type LogisticRegressionModel

Modifier and Type	Method and Description
<P> void	LogisticRegressionModel.saveModel(Exporter<LogisticRegressionModel,P> exporter, P path) Save model by the given path.