Run
10462511

Run 10462511

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 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=sklearn.cluster._agglomerative.FeatureAggl omeration,step_1=sklearn.ensemble._hist_gradient_boosting.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_regularization0.00024955336864965184
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.0028825316413386335
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins21
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth294
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter235
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes2311
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf260
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change33
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"recall_micro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.04380440813346048
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.15885347443411527
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity"l2"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_compute_full_treetrue
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_connectivitynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_distance_thresholdnull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_linkage"complete"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memorynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters796
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.amax"}
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=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"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,step_1=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
0.8725 ± 0.0267
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7884 ± 0.0258
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5766 ± 0.0515
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.077 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4718 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7884 ± 0.0258
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7884 ± 0.0257
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7884 ± 0.0258
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9438 ± 0.0045
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.473 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9461 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7883 ± 0.0256
Cross-validation details (10-fold Crossvalidation)