Run
10457228

Run 10457228

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.de composition._fastica.FastICA,step_2=sklearn.ensemble._hist_gradient_boostin g.gradient_boosting.HistGradientBoostingClassifier)(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.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization4.105519458758267e-07
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate1.3547805657780716
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins128
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth5
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter429
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes8
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf285
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change72
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"precision_micro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.061943747839047464
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.2374850777208241
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.decomposition._fastica.FastICA(1)_algorithm"deflation"
sklearn.decomposition._fastica.FastICA(1)_fun"cube"
sklearn.decomposition._fastica.FastICA(1)_fun_argsnull
sklearn.decomposition._fastica.FastICA(1)_max_iter372
sklearn.decomposition._fastica.FastICA(1)_n_components5
sklearn.decomposition._fastica.FastICA(1)_random_state42
sklearn.decomposition._fastica.FastICA(1)_tol0.1285907717431862
sklearn.decomposition._fastica.FastICA(1)_w_initnull
sklearn.decomposition._fastica.FastICA(1)_whitentrue
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(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.decomposition._fastica.FastICA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(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
0.9563 ± 0.0338
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9112 ± 0.0311
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7366 ± 0.0919
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6599 ± 0.0833
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1115 ± 0.0225
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9135 ± 0.0301
Cross-validation details (10-fold Crossvalidation)
Number of instances
1156
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9112 ± 0.0319
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9135 ± 0.0301
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.323 ± 0.0651
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
0.2438 ± 0.0444
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5872 ± 0.1069
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8508 ± 0.0469
Cross-validation details (10-fold Crossvalidation)