Run
9201660

Run 9201660

Task 9952 (Supervised Classification) phoneme Uploaded 17-05-2018 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]
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_state1085
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]
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
0.919 ± 0.0442
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9101 ± 0.018
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7824 ± 0.0446
Cross-validation details (10-fold Crossvalidation)
KB relative information score
3671.9024 ± 46.5687
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1343 ± 0.0418
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
5404
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9099 ± 0.0182
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9104 ± 0.0174
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8733
Cross-validation details (10-fold Crossvalidation)
Recall
0.9104 ± 0.0174
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3239 ± 0.1008
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2798 ± 0.0279
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6144 ± 0.0614
Cross-validation details (10-fold Crossvalidation)