FindBestModel

class FindBestModel.BestModel(java_model=None)[source]

Bases: mmlspark.Utils.ComplexParamsMixin, pyspark.ml.wrapper.JavaModel, pyspark.ml.util.JavaMLWritable, pyspark.ml.util.JavaMLReadable

Model fitted by FindBestModel.

This class is left empty on purpose. All necessary methods are exposed through inheritance.

static getJavaPackage()[source]

Returns package name String.

classmethod read()[source]

Returns an MLReader instance for this class.

class FindBestModel.FindBestModel(evaluationMetric='accuracy', models=None)[source]

Bases: mmlspark.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator

Evaluates and chooses the best model from a list of models.

Parameters:
  • evaluationMetric (str) – Metric to evaluate models with (default: accuracy)
  • models (object) – List of models to be evaluated
getEvaluationMetric()[source]
Returns:Metric to evaluate models with (default: accuracy)
Return type:str
static getJavaPackage()[source]

Returns package name String.

getModels()[source]
Returns:List of models to be evaluated
Return type:object
classmethod read()[source]

Returns an MLReader instance for this class.

setEvaluationMetric(value)[source]
Parameters:evaluationMetric (str) – Metric to evaluate models with (default: accuracy)
setModels(value)[source]
Parameters:models (object) – List of models to be evaluated
setParams(evaluationMetric='accuracy', models=None)[source]

Set the (keyword only) parameters

Parameters:
  • evaluationMetric (str) – Metric to evaluate models with (default: accuracy)
  • models (object) – List of models to be evaluated