Run
9201658

Run 9201658

Task 3902 (Supervised Classification) pc4 Uploaded 16-05-2018 by Jeroen van Hoof
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Flow

arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalim puter=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preproce ssing.data.OneHotEncoder))(2)Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(21)_categorical_features[false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false]
sklearn.preprocessing.data.OneHotEncoder(21)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(21)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(21)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(21)_sparsefalse
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_crossover_rate0.1
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_cv5
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_disable_update_checkfalse
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_early_stopnull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_generations1000000
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_max_eval_time_mins1
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_max_time_mins6
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_memorynull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_mutation_rate0.9
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_n_jobs1
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_offspring_size100
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_periodic_checkpoint_foldernull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_population_size100
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_random_state62152
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_refittrue
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_retry_on_errortrue
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_scoringnull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_subsample1.0
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_verbosefalse
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_verbosity0
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder))(2)_warm_startfalse
sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)(3)_memorynull
arbok.preprocessing.ConditionalImputer(3)_axis0
arbok.preprocessing.ConditionalImputer(3)_categorical_features[false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false]
arbok.preprocessing.ConditionalImputer(3)_copytrue
arbok.preprocessing.ConditionalImputer(3)_fill_emptynull
arbok.preprocessing.ConditionalImputer(3)_missing_values"NaN"
arbok.preprocessing.ConditionalImputer(3)_strategy"mean"
arbok.preprocessing.ConditionalImputer(3)_strategy_nominal"most_frequent"
arbok.preprocessing.ConditionalImputer(3)_verbose0

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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

17 Evaluation measures

Area under ROC curve
0.9104 ± 0.0167
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8912 ± 0.0258
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4517 ± 0.1247
Cross-validation details (10-fold Crossvalidation)
KB relative information score
444.529 ± 10.8983
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1219 ± 0.0169
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2148 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Number of instances
1458
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.889 ± 0.0258
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9012 ± 0.025
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5368
Cross-validation details (10-fold Crossvalidation)
Recall
0.9012 ± 0.025
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.5678 ± 0.0757
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3274 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2612 ± 0.0284
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7978 ± 0.0844
Cross-validation details (10-fold Crossvalidation)