Package | Description |
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org.apache.ignite.ml.regressions.logistic |
Contains various logistic regressions.
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Modifier and Type | Method and Description |
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LogisticRegressionModel |
LogisticRegressionModel.LogisticRegressionJSONExportModel.convert()
Convert JSON string to IgniteModel object.
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<K,V> LogisticRegressionModel |
LogisticRegressionSGDTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Trains model based on the specified data.
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static LogisticRegressionModel |
LogisticRegressionModel.fromJSON(Path path)
Loads KMeansModel from JSON file.
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protected <K,V> LogisticRegressionModel |
LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
|
LogisticRegressionModel |
LogisticRegressionModel.withIntercept(double intercept)
Set up the intercept.
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LogisticRegressionModel |
LogisticRegressionModel.withRawLabels(boolean isKeepingRawLabels)
Set up the output label format.
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LogisticRegressionModel |
LogisticRegressionModel.withThreshold(double threshold)
Set up the threshold.
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LogisticRegressionModel |
LogisticRegressionModel.withWeights(Vector weights)
Set up the weights.
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Modifier and Type | Method and Description |
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boolean |
LogisticRegressionSGDTrainer.isUpdateable(LogisticRegressionModel mdl) |
protected <K,V> LogisticRegressionModel |
LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> extractor)
Gets state of model in arguments, update in according to new data and return new model.
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Modifier and Type | Method and Description |
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<P> void |
LogisticRegressionModel.saveModel(Exporter<LogisticRegressionModel,P> exporter,
P path)
Save model by the given path.
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Ignite Database and Caching Platform : ver. 2.12.0 Release Date : January 10 2022