Run
10456934

Run 10456934

Task 3797 (Supervised Classification) socmob Uploaded 19-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.pr eprocessing._data.RobustScaler,step_2=sklearn.discriminant_analysis.LinearD iscriminantAnalysis)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components205
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.8598263517745337
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol1.8314504752122156e-06
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.preprocessing._data.RobustScaler(1)_copyfalse
sklearn.preprocessing._data.RobustScaler(1)_quantile_range[18.425996421882594, 33.71993472611684]
sklearn.preprocessing._data.RobustScaler(1)_with_centeringfalse
sklearn.preprocessing._data.RobustScaler(1)_with_scalingtrue
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_verbosefalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

0.9612 ± 0.0084
Per class
Cross-validation details (10-fold Crossvalidation)
0.8673 ± 0.0212
Per class
Cross-validation details (10-fold Crossvalidation)
0.5873 ± 0.07
Cross-validation details (10-fold Crossvalidation)
0.6144 ± 0.0513
Cross-validation details (10-fold Crossvalidation)
0.1191 ± 0.0138
Cross-validation details (10-fold Crossvalidation)
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.8824 ± 0.0175
Cross-validation details (10-fold Crossvalidation)
1156
Per class
Cross-validation details (10-fold Crossvalidation)
0.8911 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
0.8824 ± 0.0175
Cross-validation details (10-fold Crossvalidation)
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
0.3452 ± 0.0407
Cross-validation details (10-fold Crossvalidation)
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.3075 ± 0.0249
Cross-validation details (10-fold Crossvalidation)
0.7406 ± 0.0614
Cross-validation details (10-fold Crossvalidation)
0.7428 ± 0.0347
Cross-validation details (10-fold Crossvalidation)