Run
10459574

Run 10459574

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 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_regularization2.9124799399643278e-06
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.6044553042964568
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins43
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth2
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter881
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes8901
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf5835
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change59
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"precision_weighted"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.19373971793907935
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.13629613886190145
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.

18 Evaluation measures

Area under ROC curve
0.7808 ± 0.0062
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7363 ± 0.0098
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3649 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2076 ± 0.0114
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3364 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7482 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Number of instances
16599
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7354 ± 0.0107
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7482 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8921 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7876 ± 0.0094
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.41 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8873 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6694 ± 0.0106
Cross-validation details (10-fold Crossvalidation)