Run
10454053

Run 10454053

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_regularization1.0576448323378076e-05
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.010060522475693034
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins184
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth14
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter963
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes162
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf341
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change0
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)_tol0.0574310987296516
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.41113882985283673
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
0.8252 ± 0.0226
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5421 ± 0.0396
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4207 ± 0.0472
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3606 ± 0.0273
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2821 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3748 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.565 ± 0.0353
Cross-validation details (10-fold Crossvalidation)
Number of instances
846
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5383 ± 0.0476
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.565 ± 0.0353
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.9991 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7525 ± 0.0262
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3658 ± 0.0085
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.845 ± 0.0196
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5698 ± 0.0372
Cross-validation details (10-fold Crossvalidation)