OpenML
9200053

Run 9200053

Task 3656 (Supervised Classification) diabetes_numeric Uploaded 06-05-2018 by Benjamin Strang
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  • openml-python Sklearn_0.19.1. study_123
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeli ne.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one- hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding= sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scal ing=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_net work.multilayer_perceptron.MLPClassifier))(1)Automatically created scikit-learn flow.
mylib.preprocessing_openml14.ConditionalImputer(1)_axis0
mylib.preprocessing_openml14.ConditionalImputer(1)_categorical_features[]
mylib.preprocessing_openml14.ConditionalImputer(1)_copytrue
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mylib.preprocessing_openml14.ConditionalImputer(1)_strategy"median"
mylib.preprocessing_openml14.ConditionalImputer(1)_strategy_nominal"most_frequent"
mylib.preprocessing_openml14.ConditionalImputer(1)_verbose0
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features[]
sklearn.preprocessing.data.OneHotEncoder(17)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(17)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(17)_sparsefalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold0.0
sklearn.preprocessing.data.StandardScaler(5)_copytrue
sklearn.preprocessing.data.StandardScaler(5)_with_meantrue
sklearn.preprocessing.data.StandardScaler(5)_with_stdtrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_activation"logistic"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_alpha0.0001
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_batch_size"auto"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_beta_10.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_beta_20.999
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_early_stoppingfalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_epsilon1e-08
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_hidden_layer_sizes[100]
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_learning_rate"constant"
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sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_max_iter2000
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_momentum0.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_nesterovs_momentumtrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_power_t0.5
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_random_state3
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_shuffletrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_solver"adam"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_tol0.0001
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_validation_fraction0.1
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_verbosefalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(10)_warm_startfalse
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_cv3
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_error_score"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_fit_paramsnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_iidtrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_n_iter250
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_n_jobs-1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_param_distributions{"classifier__alpha": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 1e-07, "b": 0.1, "args": [], "kwds": {"base": 10, "low": 1e-07, "high": 0.1}}}, "classifier__early_stopping": [true, false], "classifier__hidden_layer_sizes": [[128, 128], [128], [64, 64], [64], [32, 32], [32]], "classifier__learning_rate_init": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 1e-05, "b": 1, "args": [], "kwds": {"base": 10, "low": 1e-05, "high": 1}}}, "classifier__tol": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 1e-05, "b": 0.1, "args": [], "kwds": {"base": 10, "low": 1e-05, "high": 0.1}}}, "imputation__strategy": ["mean", "median", "most_frequent"]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_pre_dispatch"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_random_state3
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_refittrue
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_return_train_score"warn"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_scoringnull
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier))(1)_verbose0
sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1)_memorynull

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.

17 Evaluation measures

Area under ROC curve
0.7443 ± 0.3471
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6744 ± 0.2699
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.319 ± 0.5396
Cross-validation details (10-fold Crossvalidation)
KB relative information score
6.8218 ± 1.1836
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.409 ± 0.102
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4791 ± 0.0218
Cross-validation details (10-fold Crossvalidation)
Number of instances
43
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6744 ± 0.2495
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6744 ± 0.2667
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.971
Cross-validation details (10-fold Crossvalidation)
Recall
0.6744 ± 0.2667
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8537 ± 0.2247
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4889 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4541 ± 0.0973
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9286 ± 0.2119
Cross-validation details (10-fold Crossvalidation)