Run 8858313

Task 146197 (Supervised Classification) dna Uploaded 21-02-2018 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.tree.tree.DecisionTreeClassifier)(4)	Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(6)_axis	0
openmlstudy14.preprocessing.ConditionalImputer(6)_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, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]
openmlstudy14.preprocessing.ConditionalImputer(6)_copy	true
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty	0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values	"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy	"median"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal	"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose	0
sklearn.preprocessing.data.OneHotEncoder(18)_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, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]
sklearn.preprocessing.data.OneHotEncoder(18)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(18)_handle_unknown	"ignore"
sklearn.preprocessing.data.OneHotEncoder(18)_n_values	"auto"
sklearn.preprocessing.data.OneHotEncoder(18)_sparse	true
sklearn.feature_selection.variance_threshold.VarianceThreshold(12)_threshold	0.0
sklearn.tree.tree.DecisionTreeClassifier(19)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(19)_criterion	"gini"
sklearn.tree.tree.DecisionTreeClassifier(19)_max_depth	null
sklearn.tree.tree.DecisionTreeClassifier(19)_max_features	null
sklearn.tree.tree.DecisionTreeClassifier(19)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(19)_min_impurity_split	1e-07
sklearn.tree.tree.DecisionTreeClassifier(19)_min_samples_leaf	1
sklearn.tree.tree.DecisionTreeClassifier(19)_min_samples_split	2
sklearn.tree.tree.DecisionTreeClassifier(19)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(19)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(19)_random_state	1
sklearn.tree.tree.DecisionTreeClassifier(19)_splitter	"best"