Run
10457755

Run 10457755

Task 3797 (Supervised Classification) socmob Uploaded 19-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • automl_meta_features openml-python Sklearn_0.22.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_ pca.KernelPCA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boos ting.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.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization7.78279340613442e-06
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.762125749906601
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins16
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth2
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter415
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes771
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf4
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change76
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"precision_micro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.1953125269790996
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.5970019030168676
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"dense"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transformfalse
sklearn.decomposition._kernel_pca.KernelPCA(1)_gamma1.569281682936436e-08
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel"rbf"
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel_paramsnull
sklearn.decomposition._kernel_pca.KernelPCA(1)_max_iter285
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components33
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)_tol0
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,step_2=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
0.9043 ± 0.0276
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8542 ± 0.1268
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6009 ± 0.2042
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.4617 ± 0.5029
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1623 ± 0.1477
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3451 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8478 ± 0.1385
Cross-validation details (10-fold Crossvalidation)
Number of instances
1156
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8687 ± 0.0348
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8478 ± 0.1385
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7628 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4703 ± 0.4282
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4152 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.364 ± 0.1419
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8766 ± 0.3422
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8323 ± 0.0607
Cross-validation details (10-fold Crossvalidation)