Run
10456717

Run 10456717

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.sv m._classes.SVC)(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.svm._classes.SVC(4)_C8.379237551051565e-07
sklearn.svm._classes.SVC(4)_break_tiestrue
sklearn.svm._classes.SVC(4)_cache_size200
sklearn.svm._classes.SVC(4)_class_weightnull
sklearn.svm._classes.SVC(4)_coef00.0
sklearn.svm._classes.SVC(4)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(4)_degree3
sklearn.svm._classes.SVC(4)_gamma2.152383916184358e-06
sklearn.svm._classes.SVC(4)_kernel"rbf"
sklearn.svm._classes.SVC(4)_max_iter-1
sklearn.svm._classes.SVC(4)_probabilitytrue
sklearn.svm._classes.SVC(4)_random_state42
sklearn.svm._classes.SVC(4)_shrinkingfalse
sklearn.svm._classes.SVC(4)_tol0.003355787310300607
sklearn.svm._classes.SVC(4)_verbosefalse
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.svm._classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.svm._classes.SVC)(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"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.svm._classes.SVC)(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

0.9646 ± 0.0087
Per class
Cross-validation details (10-fold Crossvalidation)
0.9234 ± 0.0128
Per class
Cross-validation details (10-fold Crossvalidation)
0.7745 ± 0.0391
Cross-validation details (10-fold Crossvalidation)
0.6583 ± 0.0408
Cross-validation details (10-fold Crossvalidation)
0.1207 ± 0.0115
Cross-validation details (10-fold Crossvalidation)
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
0.9247 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
1156
Per class
Cross-validation details (10-fold Crossvalidation)
0.9232 ± 0.0133
Per class
Cross-validation details (10-fold Crossvalidation)
0.9247 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
0.3497 ± 0.0334
Cross-validation details (10-fold Crossvalidation)
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.2383 ± 0.0243
Cross-validation details (10-fold Crossvalidation)
0.5738 ± 0.0576
Cross-validation details (10-fold Crossvalidation)
0.8748 ± 0.0283
Cross-validation details (10-fold Crossvalidation)