Run
9534290

Run 9534290

Task 3889 (Supervised Classification) sylva_agnostic Uploaded 11-10-2018 by Jan van Rijn
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=s klearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imp uter,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=skle arn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotenco der=sklearn.preprocessing._encoders.OneHotEncoder)),mlpclassifier=sklearn.n eural_network.multilayer_perceptron.MLPClassifier)(1)Automatically created scikit-learn flow.
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weightsnull
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
sklearn.impute.MissingIndicator(1)_error_on_newfalse
sklearn.impute.MissingIndicator(1)_features"missing-only"
sklearn.impute.MissingIndicator(1)_missing_valuesNaN
sklearn.impute.MissingIndicator(1)_sparse"auto"
sklearn.preprocessing.imputation.Imputer(29)_axis0
sklearn.preprocessing.imputation.Imputer(29)_copytrue
sklearn.preprocessing.imputation.Imputer(29)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(29)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(29)_verbose0
sklearn.preprocessing.data.StandardScaler(14)_copytrue
sklearn.preprocessing.data.StandardScaler(14)_with_meantrue
sklearn.preprocessing.data.StandardScaler(14)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.impute.SimpleImputer(1)_copytrue
sklearn.impute.SimpleImputer(1)_fill_value-1
sklearn.impute.SimpleImputer(1)_missing_valuesNaN
sklearn.impute.SimpleImputer(1)_strategy"constant"
sklearn.impute.SimpleImputer(1)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(3)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(3)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1)_memorynull
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_activation"logistic"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_alpha0.005279876255917719
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_batch_size1599
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_beta_10.897436112025623
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_beta_20.12549094242189618
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_early_stoppingfalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_epsilon1e-08
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_hidden_layer_sizes919
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_learning_rate"constant"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_learning_rate_init0.002569699233493726
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_max_iter205
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_momentum0.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_n_iter_no_change231
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_nesterovs_momentumtrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_power_t0.5
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_random_state43608
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_shufflefalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_solver"adam"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_tol0.005231884647969349
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_validation_fraction0.1
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_verbosefalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_warm_startfalse

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.

15 Evaluation measures

Area under ROC curve
0.4986
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
855.9107 ± 161.6268
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0975 ± 0.0195
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1156 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
14395
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9385 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3338
Cross-validation details (10-fold Crossvalidation)
Recall
0.9385 ± 0.0003
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8437 ± 0.1705
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2403 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2422 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0077 ± 0.008
Cross-validation details (10-fold Crossvalidation)