Run
10459591

Run 10459591

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_regularization2.3060234829810002e-07
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.13407653876339157
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins168
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth2
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter159
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes12784
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf4095
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change96
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"f1_macro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.09545679710389404
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.09590299313726754
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_power250
sklearn.decomposition._pca.PCA(1)_n_components3
sklearn.decomposition._pca.PCA(1)_random_state42
sklearn.decomposition._pca.PCA(1)_svd_solver"randomized"
sklearn.decomposition._pca.PCA(1)_tol2.1320885187686316
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
0.6124 ± 0.0136
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5947 ± 0.0122
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.047 ± 0.0224
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0243 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4111 ± 0.0017
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6943 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Number of instances
16599
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6543 ± 0.025
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6943 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8921 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9626 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4537 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9818 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5176 ± 0.0086
Cross-validation details (10-fold Crossvalidation)