Run
10462307

Run 10462307

Task 3891 (Supervised Classification) gina_agnostic Uploaded 21-05-2020 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.util.sklearn.StackingEstimator(esti mator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradie ntBoostingClassifier),step_1=sklearn.tree._classes.DecisionTreeClassifier)( 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.tree._classes.DecisionTreeClassifier(3)_ccp_alpha0.04815706298620015
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth492
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features0.8355946560072949
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes1033
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease0.0037875720384883405
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf0.14460665864425826
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split0.3988616627820223
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf0.2118265684412295
sklearn.tree._classes.DecisionTreeClassifier(3)_presort"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter"best"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization1.1289364725865456e-05
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.00029856733735454395
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins143
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth454
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter661
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes1708
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf705
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change86
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)_tol0.05660288681201531
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.3599802516128163
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_1=sklearn.tree._classes.DecisionTreeClassifier)(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=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_1=sklearn.tree._classes.DecisionTreeClassifier)(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
0.7947 ± 0.044
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7453 ± 0.0348
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4912 ± 0.0659
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3256 ± 0.0494
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3554 ± 0.0229
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7454 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7467 ± 0.029
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7454 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7109 ± 0.0459
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4227 ± 0.0224
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8456 ± 0.0447
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7459 ± 0.0329
Cross-validation details (10-fold Crossvalidation)