Run
10462389

Run 10462389

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 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.impute._knn.KNNImputer,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_regularization7.912654574688506e-06
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.041698863431282575
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins5
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth385
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter761
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes2125
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf281
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change66
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"roc_auc_ovo"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.048424198318809564
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.2700775308873725
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.impute._knn.KNNImputer(1)_add_indicatorfalse
sklearn.impute._knn.KNNImputer(1)_copyfalse
sklearn.impute._knn.KNNImputer(1)_metric"nan_euclidean"
sklearn.impute._knn.KNNImputer(1)_missing_valuesNaN
sklearn.impute._knn.KNNImputer(1)_n_neighbors25
sklearn.impute._knn.KNNImputer(1)_weights"uniform"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,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.impute._knn.KNNImputer,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
0.9584 ± 0.0108
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8985 ± 0.0216
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.797 ± 0.0431
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6073 ± 0.0255
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2175 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8985 ± 0.0216
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8988 ± 0.0213
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8985 ± 0.0216
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4352 ± 0.0242
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2904 ± 0.0158
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5809 ± 0.0316
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8987 ± 0.0215
Cross-validation details (10-fold Crossvalidation)