Run
10459672

Run 10459672

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.decomposition._pca.PCA,step_1=skle arn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoosting Classifier)(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.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization0.14894936372174758
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.5234270046027927
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins138
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth6
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter364
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes6060
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf3363
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change49
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)_tol0.00811104663310036
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.40879249310907206
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.decomposition._pca.PCA(1)_copyfalse
sklearn.decomposition._pca.PCA(1)_iterated_power"auto"
sklearn.decomposition._pca.PCA(1)_n_components7
sklearn.decomposition._pca.PCA(1)_random_state42
sklearn.decomposition._pca.PCA(1)_svd_solver"arpack"
sklearn.decomposition._pca.PCA(1)_tol2.5269157197652814
sklearn.decomposition._pca.PCA(1)_whitenfalse
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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=sklearn.decomposition._pca.PCA,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.

18 Evaluation measures

Area under ROC curve
0.7273 ± 0.0091
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6892 ± 0.0074
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2437 ± 0.0181
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1383 ± 0.0127
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3647 ± 0.0044
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7225 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Number of instances
16599
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7005 ± 0.0082
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7225 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8921 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.854 ± 0.0104
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4286 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9275 ± 0.0068
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6047 ± 0.0083
Cross-validation details (10-fold Crossvalidation)