Run 9332304

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 10-10-2018 by Jan van Rijn
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

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)),adaboostclassifier=skle arn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree .tree.DecisionTreeClassifier))(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_jobs	null
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_threshold	0.3
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_weights	null
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memory	null
sklearn.impute.MissingIndicator(1)_error_on_new	false
sklearn.impute.MissingIndicator(1)_features	"missing-only"
sklearn.impute.MissingIndicator(1)_missing_values	NaN
sklearn.impute.MissingIndicator(1)_sparse	"auto"
sklearn.preprocessing.imputation.Imputer(29)_axis	0
sklearn.preprocessing.imputation.Imputer(29)_copy	true
sklearn.preprocessing.imputation.Imputer(29)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(29)_strategy	"most_frequent"
sklearn.preprocessing.imputation.Imputer(29)_verbose	0
sklearn.preprocessing.data.StandardScaler(14)_copy	true
sklearn.preprocessing.data.StandardScaler(14)_with_mean	true
sklearn.preprocessing.data.StandardScaler(14)_with_std	true
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
sklearn.impute.SimpleImputer(1)_copy	true
sklearn.impute.SimpleImputer(1)_fill_value	-1
sklearn.impute.SimpleImputer(1)_missing_values	NaN
sklearn.impute.SimpleImputer(1)_strategy	"constant"
sklearn.impute.SimpleImputer(1)_verbose	0
sklearn.preprocessing._encoders.OneHotEncoder(3)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_categories	null
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_values	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparse	true
sklearn.tree.tree.DecisionTreeClassifier(29)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(29)_criterion	"gini"
sklearn.tree.tree.DecisionTreeClassifier(29)_max_depth	4
sklearn.tree.tree.DecisionTreeClassifier(29)_max_features	null
sklearn.tree.tree.DecisionTreeClassifier(29)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_decrease	0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_split	null
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_leaf	1
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_split	2
sklearn.tree.tree.DecisionTreeClassifier(29)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(29)_random_state	50036
sklearn.tree.tree.DecisionTreeClassifier(29)_splitter	"best"
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)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memory	null
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_algorithm	"SAMME"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_learning_rate	0.11320765376744121
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_n_estimators	398
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(8)_random_state	47548