Run
10462652

Run 10462652

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=automl.util.sklearn.StackingEstimator(esti mator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.discrimi nant_analysis.LinearDiscriminantAnalysis)(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_alpha0.33324289350209724
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth900
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features0.3429277669133419
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes392
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease0.013195227788599363
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf0.12590075678133258
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split0.42189250004128176
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf0.2685429521748486
sklearn.tree._classes.DecisionTreeClassifier(3)_presort"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter"random"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components331
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.07026243476119565
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"eigen"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.006067389418119532
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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.tree._classes.DecisionTreeClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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
0.9295 ± 0.0159
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.861 ± 0.0185
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7219 ± 0.0369
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.677 ± 0.0331
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1675 ± 0.016
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.861 ± 0.0185
Cross-validation details (10-fold Crossvalidation)
Number of instances
3468
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8611 ± 0.0185
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.861 ± 0.0185
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9998 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3351 ± 0.032
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4999 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3243 ± 0.0252
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6487 ± 0.0505
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8608 ± 0.0183
Cross-validation details (10-fold Crossvalidation)