Run
10457879

Run 10457879

Task 9900 (Supervised Classification) abalone 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=automl.util.sklearn.StackingEs timator(estimator=sklearn.svm._classes.SVC),step_2=sklearn.ensemble._hist_g radient_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.svm._classes.SVC(4)_C0.15101546222595566
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)_coef0-13.627121472286014
sklearn.svm._classes.SVC(4)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(4)_degree3
sklearn.svm._classes.SVC(4)_gamma1.2866338146821647e-05
sklearn.svm._classes.SVC(4)_kernel"sigmoid"
sklearn.svm._classes.SVC(4)_max_iter-1
sklearn.svm._classes.SVC(4)_probabilityfalse
sklearn.svm._classes.SVC(4)_random_state42
sklearn.svm._classes.SVC(4)_shrinkingtrue
sklearn.svm._classes.SVC(4)_tol0.00039865298091708415
sklearn.svm._classes.SVC(4)_verbosefalse
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization1.2090697341272889e-05
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.02698508580536454
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins104
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth7
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter624
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes2546
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf1494
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change7
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"f1_weighted"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.24344269850149172
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.1928884165787904
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.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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.

16 Evaluation measures

Area under ROC curve
0.7104 ± 0.0097
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3527 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0932 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4218 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5739 ± 0.0104
Cross-validation details (10-fold Crossvalidation)
Number of instances
4177
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5739 ± 0.0104
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.584 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9496 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4504 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9559 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5604 ± 0.0098
Cross-validation details (10-fold Crossvalidation)