Run
10457618

Run 10457618

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.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._fastica .FastICA,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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_components297
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.3269219810705377
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"eigen"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.0350474600819841
sklearn.decomposition._fastica.FastICA(1)_algorithm"parallel"
sklearn.decomposition._fastica.FastICA(1)_fun"cube"
sklearn.decomposition._fastica.FastICA(1)_fun_argsnull
sklearn.decomposition._fastica.FastICA(1)_max_iter932
sklearn.decomposition._fastica.FastICA(1)_n_components21
sklearn.decomposition._fastica.FastICA(1)_random_state42
sklearn.decomposition._fastica.FastICA(1)_tol0.4954181846765453
sklearn.decomposition._fastica.FastICA(1)_w_initnull
sklearn.decomposition._fastica.FastICA(1)_whitentrue
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,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.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,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

Area under ROC curve
0.9472 ± 0.021
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9011 ± 0.0214
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6992 ± 0.0675
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5411 ± 0.0468
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1557 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9074 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Number of instances
1156
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9081 ± 0.0202
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9074 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4513 ± 0.0328
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2747 ± 0.0211
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6615 ± 0.0506
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.812 ± 0.0407
Cross-validation details (10-fold Crossvalidation)