Run
9268202

Run 9268202

Task 3492 (Supervised Classification) monks-problems-1 Uploaded 09-10-2018 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)),sgdclassifier=sklearn.l inear_model.stochastic_gradient.SGDClassifier)(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)),sgdclassifier=sklearn.linear_model.stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_alpha4.036087309574866e-05
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_averagefalse
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_class_weightnull
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_early_stoppingfalse
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_epsilon0.0009372165873713245
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_eta00.0
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_fit_intercepttrue
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_l1_ratio0.15
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_learning_rate"optimal"
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_loss"modified_huber"
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_max_iternull
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_n_iternull
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_n_iter_no_change5
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_n_jobsnull
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_penalty"l1"
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_power_t0.5
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_random_state11384
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_shuffletrue
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_tol0.012574783326770643
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_validation_fraction0.1
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_verbose0
sklearn.linear_model.stochastic_gradient.SGDClassifier(8)_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.

17 Evaluation measures

Area under ROC curve
0.6949 ± 0.1152
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6938 ± 0.1229
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3885 ± 0.2253
Cross-validation details (10-fold Crossvalidation)
KB relative information score
210.0787 ± 13.2479
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3118 ± 0.1203
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Number of instances
556
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6955 ± 0.1261
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6942 ± 0.1128
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Recall
0.6942 ± 0.1128
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6237 ± 0.2406
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5489 ± 0.1031
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0978 ± 0.2062
Cross-validation details (10-fold Crossvalidation)