Run
10455611

Run 10455611

Task 9914 (Supervised Classification) wilt 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.util.sklearn.StackingEstimator(esti mator=sklearn.svm._classes.SVC),step_1=sklearn.ensemble._hist_gradient_boos ting.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.svm._classes.SVC(4)_C2383.50183229838
sklearn.svm._classes.SVC(4)_break_tiesfalse
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"ovo"
sklearn.svm._classes.SVC(4)_degree3
sklearn.svm._classes.SVC(4)_gamma9.68795498923458e-05
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)_shrinkingtrue
sklearn.svm._classes.SVC(4)_tol0.0007056249021923254
sklearn.svm._classes.SVC(4)_verbosefalse
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization2.7291003267638022e-05
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.026809605783135092
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins8
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth2
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter877
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes1028
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf2008
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change61
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"precision_macro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.16639015010481317
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.27211407760076783
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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.

16 Evaluation measures

Area under ROC curve
0.4981
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0009 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.102 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9983 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)