Run
9387715

Run 9387715

Task 125920 (Supervised Classification) dresses-sales Uploaded 10-10-2018 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"median"
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"identity"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_alpha0.00169862336732676
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_batch_size"auto"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_beta_10.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_beta_20.999
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_sizes183
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_learning_rate"invscaling"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_learning_rate_init0.001
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_max_iter642
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_momentum0.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_n_iter_no_change10
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_state50072
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_shuffletrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_solver"lbfgs"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_tol0.035456458216672905
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.

17 Evaluation measures

Area under ROC curve
0.5467 ± 0.1028
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5208 ± 0.0981
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0572 ± 0.1574
Cross-validation details (10-fold Crossvalidation)
KB relative information score
12.052 ± 2.2267
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4766 ± 0.0172
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4873
Cross-validation details (10-fold Crossvalidation)
Number of instances
500
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5579 ± 0.1486
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.582 ± 0.0614
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9816
Cross-validation details (10-fold Crossvalidation)
Recall
0.582 ± 0.0614
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9781 ± 0.0353
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4936
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4922 ± 0.0133
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9972 ± 0.027
Cross-validation details (10-fold Crossvalidation)