Run 9010590

Task 125966 (Supervised Classification) SpeedDating Uploaded 10-04-2018 by Hilde Weerts
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Flow

sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition alImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=skle arn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sel ection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)	Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features	[0, 2, 6, 7, 8, 9, 12, 13, 14, 21, 22, 23, 24, 25, 26, 33, 34, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 56, 57, 58, 59, 60, 67, 68, 69, 70, 71, 72, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 112, 113, 114, 117, 118]
sklearn.preprocessing.data.OneHotEncoder(17)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown	"ignore"
sklearn.preprocessing.data.OneHotEncoder(17)_n_values	"auto"
sklearn.preprocessing.data.OneHotEncoder(17)_sparse	true
sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold	0.0
sklearn.preprocessing.data.StandardScaler(5)_copy	true
sklearn.preprocessing.data.StandardScaler(5)_with_mean	false
sklearn.preprocessing.data.StandardScaler(5)_with_std	true
sklearn.svm.classes.SVC(16)_C	1.487798517281704
sklearn.svm.classes.SVC(16)_cache_size	200
sklearn.svm.classes.SVC(16)_class_weight	null
sklearn.svm.classes.SVC(16)_coef0	0.278658958251063
sklearn.svm.classes.SVC(16)_decision_function_shape	"ovr"
sklearn.svm.classes.SVC(16)_degree	3
sklearn.svm.classes.SVC(16)_gamma	0.0009873582400158423
sklearn.svm.classes.SVC(16)_kernel	"rbf"
sklearn.svm.classes.SVC(16)_max_iter	-1
sklearn.svm.classes.SVC(16)_probability	false
sklearn.svm.classes.SVC(16)_random_state	1
sklearn.svm.classes.SVC(16)_shrinking	false
sklearn.svm.classes.SVC(16)_tol	0.0001302074497899874
sklearn.svm.classes.SVC(16)_verbose	false
hyperimp.utils.preprocessing.ConditionalImputer(1)_axis	0
hyperimp.utils.preprocessing.ConditionalImputer(1)_categorical_features	[0, 2, 6, 7, 8, 9, 12, 13, 14, 21, 22, 23, 24, 25, 26, 33, 34, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 56, 57, 58, 59, 60, 67, 68, 69, 70, 71, 72, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 112, 113, 114, 117, 118]
hyperimp.utils.preprocessing.ConditionalImputer(1)_copy	true
hyperimp.utils.preprocessing.ConditionalImputer(1)_fill_empty	0
hyperimp.utils.preprocessing.ConditionalImputer(1)_missing_values	"NaN"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy	"mean"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy_nominal	"most_frequent"
hyperimp.utils.preprocessing.ConditionalImputer(1)_verbose	0
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)_memory	null