Run
10456246

Run 10456246

Task 9914 (Supervised Classification) wilt 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.util.sklearn.StackingEstimator(esti mator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.ensem ble._weight_boosting.AdaBoostClassifier)(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._forest.RandomForestClassifier(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.49264262601692876
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth5
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1774
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.5702308841101069
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.6877657223619105
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.2606232128813255
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.49660818309007315
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.4302676098278511
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1176
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scoretrue
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate0.06951378241325641
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators639
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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._forest.RandomForestClassifier),step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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
0.9877 ± 0.0096
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9754 ± 0.0064
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7392 ± 0.0746
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-9.0002 ± 0.0928
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4556 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9775 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9766 ± 0.0058
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9775 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
4.457 ± 0.0283
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4561 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
2.0189 ± 0.0126
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8129 ± 0.0438
Cross-validation details (10-fold Crossvalidation)