Run
10453934

Run 10453934

Task 53 (Supervised Classification) vehicle Uploaded 18-05-2020 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.ensemble._hist_gradient_boosting.g radient_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.pipeline.Pipeline(step_0=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_verbosefalse
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization0.5274807292582125
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.0004505068984300808
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins227
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth13
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter964
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes308
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf348
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change45
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"roc_auc_ovo"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.1554542263464479
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.1902276898544832
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse

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
0.4967
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0027
KB relative information score
0.0004 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3748 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3748 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.2553 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Number of instances
846
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.2553 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.9991 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.248
Cross-validation details (10-fold Crossvalidation)