train(trainingData:featureColumns:labelColumn:recordingFileColumn:parameters:sessionParameters:)

Declaration

static func train(trainingData: MLActivityClassifier.DataSource, featureColumns: [String], labelColumn: String, recordingFileColumn: String, parameters: MLActivityClassifier.ModelParameters = ModelParameters(validationData: nil), sessionParameters: MLTrainingSessionParameters = _defaultSessionParameters) throws -> MLJob<MLActivityClassifier>

Parameters

• trainingData:

  An Datasource instance.

• featureColumns:

  The names of the columns in an annotation file that contain sensor data.

• labelColumn:

  The name of the column in an annotation file that contains the activity labels if trainingData uses Directorywithdataandannotation(at:annotationfilename:timestampcolumn:labelstarttimecolumn:labelendtimecolumn:).

  The initializer ignores this parameter if trainingData uses Labeleddirectories(at:).

• recordingFileColumn:

  The name of the column in an annotation file that contains the data filenames if trainingData uses Directorywithdataandannotation(at:annotationfilename:timestampcolumn:labelstarttimecolumn:labelendtimecolumn:).

  The initializer ignores this parameter if trainingData uses Labeleddirectories(at:).

• parameters:

  An Modelparameters Swift.struct instance you use to configure the model for the training session.

• sessionParameters:

  An Mltrainingsessionparameters instance you use to configure the training session.

Return Value

An MLJob that represents the activity classifier training session.

See Also

Training an activity classifier asynchronously

• makeTrainingSession(trainingData:featureColumns:labelColumn:recordingFileColumn:parameters:sessionParameters:)
• resume(_:)
• restoreTrainingSession(sessionParameters:)