Quality measure algorithms

Warning

Some algorithms may require the installation of GEMSEO with all its features and some others may depend on plugins.

Note

All the features of the wrapped optimization libraries may not be exposed through GEMSEO.

F1Measure

Module: gemseo.mlearning.qual_measure.f1_measure

Required parameters

• algo : MLClassificationAlgo

  A machine learning algorithm for classification.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.

MLClusteringMeasure

Module: gemseo.mlearning.qual_measure.cluster_measure

Required parameters

• algo : MLClusteringAlgo

  A machine learning algorithm for clustering.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.

MLErrorMeasure

Module: gemseo.mlearning.qual_measure.error_measure

Required parameters

• algo : MLSupervisedAlgo

  A machine learning algorithm for supervised learning.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.

MLPredictiveClusteringMeasure

Module: gemseo.mlearning.qual_measure.cluster_measure

Required parameters

• algo : MLPredictiveClusteringAlgo

  A machine learning algorithm for predictive clustering.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.

MSEMeasure

Module: gemseo.mlearning.qual_measure.mse_measure

Required parameters

• algo : MLRegressionAlgo

  A machine learning algorithm for regression.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.

R2Measure

Module: gemseo.mlearning.qual_measure.r2_measure

Required parameters

• algo : MLRegressionAlgo

  A machine learning algorithm for regression.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.

RMSEMeasure

Module: gemseo.mlearning.qual_measure.rmse_measure

Required parameters

• algo : MLRegressionAlgo

  A machine learning algorithm for regression.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.

SilhouetteMeasure

Module: gemseo.mlearning.qual_measure.silhouette

Required parameters

• algo : MLPredictiveClusteringAlgo

  A clustering algorithm.

Optional parameters

• fit_transformers : bool, optional

  Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

  By default it is set to False.