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9201796

Run 9201796

Task 146824 (Supervised Classification) mfeat-pixel Uploaded 28-05-2018 by Jeroen van Hoof
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arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalim puter=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preproce ssing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.varian ce_threshold.VarianceThreshold))(2)Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(21)_categorical_features[0]
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
sklearn.feature_selection.variance_threshold.VarianceThreshold(14)_threshold0.0
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_crossover_rate0.1
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_cv5
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_disable_update_checkfalse
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_early_stopnull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_generations1000000
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_max_eval_time_mins5
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_max_time_mins60
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_memorynull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_mutation_rate0.9
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_n_jobs1
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_offspring_size100
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_periodic_checkpoint_foldernull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_population_size100
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_random_state33949
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_refittrue
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_retry_on_errorfalse
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_scoringnull
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_subsample1.0
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_verbosetrue
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_verbosity0
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold))(2)_warm_startfalse
sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold)(2)_memorynull
arbok.preprocessing.ConditionalImputer(8)_axis0
arbok.preprocessing.ConditionalImputer(8)_categorical_features[0]
arbok.preprocessing.ConditionalImputer(8)_copytrue
arbok.preprocessing.ConditionalImputer(8)_fill_empty0
arbok.preprocessing.ConditionalImputer(8)_missing_values"NaN"
arbok.preprocessing.ConditionalImputer(8)_strategy"mean"
arbok.preprocessing.ConditionalImputer(8)_strategy_nominal"most_frequent"
arbok.preprocessing.ConditionalImputer(8)_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
0.9976 ± 0.0026
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9765 ± 0.0079
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9739 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1753.8862 ± 12.1113
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0425 ± 0.021
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Number of instances
2000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9766 ± 0.0075
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9765 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Recall
0.9765 ± 0.0078
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2362 ± 0.1168
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1 ± 0.0222
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3335 ± 0.0741
Cross-validation details (10-fold Crossvalidation)