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Task 7 (Supervised Classification) audiology Uploaded 12-08-2017 by Jan van Rijn
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Flow

sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethres hold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classif ier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=skle arn.tree.tree.DecisionTreeClassifier))(1)	Automatically created scikit-learn flow.
sklearn.tree.tree.DecisionTreeClassifier(10)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(10)_criterion	"gini"
sklearn.tree.tree.DecisionTreeClassifier(10)_max_depth	4
sklearn.tree.tree.DecisionTreeClassifier(10)_max_features	null
sklearn.tree.tree.DecisionTreeClassifier(10)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(10)_min_impurity_split	1e-07
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_leaf	1
sklearn.tree.tree.DecisionTreeClassifier(10)_min_samples_split	2
sklearn.tree.tree.DecisionTreeClassifier(10)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(10)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(10)_random_state	41829
sklearn.tree.tree.DecisionTreeClassifier(10)_splitter	"best"
openmlstudy14.preprocessing.ConditionalImputer(2)_axis	0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]
openmlstudy14.preprocessing.ConditionalImputer(2)_copy	true
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty	0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values	"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy	"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal	"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose	0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]
sklearn.preprocessing.data.OneHotEncoder(7)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(7)_handle_unknown	"ignore"
sklearn.preprocessing.data.OneHotEncoder(7)_n_values	"auto"
sklearn.preprocessing.data.OneHotEncoder(7)_sparse	true
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold	0.0
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_algorithm	"SAMME.R"
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_learning_rate	0.019766105195427455
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_n_estimators	99
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(2)_random_state	22931