Run
10458781

Run 10458781

Task 9981 (Supervised Classification) cnae-9 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._kernel_pca.KernelPC A,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGra dientBoostingClassifier)(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_regularization0.05609461542245975
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate1.2733469995734294
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins232
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth55
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter626
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes219
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf289
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change64
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)_tol0.15579051364798735
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.27755158290167015
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_alpha1.0
sklearn.decomposition._kernel_pca.KernelPCA(1)_coef01
sklearn.decomposition._kernel_pca.KernelPCA(1)_copy_Xfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_degree3
sklearn.decomposition._kernel_pca.KernelPCA(1)_eigen_solver"arpack"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transformfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_gamma4.985563370555812e-05
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel"rbf"
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel_paramsnull
sklearn.decomposition._kernel_pca.KernelPCA(1)_max_iter941
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components76
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_jobs1
sklearn.decomposition._kernel_pca.KernelPCA(1)_random_state42
sklearn.decomposition._kernel_pca.KernelPCA(1)_remove_zero_eigtrue
sklearn.decomposition._kernel_pca.KernelPCA(1)_tol1.3168284312364187
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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._kernel_pca.KernelPCA,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
0.9819 ± 0.0067
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8606 ± 0.028
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8427 ± 0.0304
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8547 ± 0.0213
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0338 ± 0.0044
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8602 ± 0.0271
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8614 ± 0.0269
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8602 ± 0.0271
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.171 ± 0.0222
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1614 ± 0.0118
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5136 ± 0.0377
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8602 ± 0.0271
Cross-validation details (10-fold Crossvalidation)