Run 9005105

Task 14952 (Supervised Classification) PhishingWebsites Uploaded 09-04-2018 by Hilde Weerts
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

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, 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]
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	1019.6559772244711
sklearn.svm.classes.SVC(16)_cache_size	200
sklearn.svm.classes.SVC(16)_class_weight	null
sklearn.svm.classes.SVC(16)_coef0	0.7496933383297202
sklearn.svm.classes.SVC(16)_decision_function_shape	"ovr"
sklearn.svm.classes.SVC(16)_degree	3
sklearn.svm.classes.SVC(16)_gamma	0.3522032409528272
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	true
sklearn.svm.classes.SVC(16)_tol	0.009971795266674712
sklearn.svm.classes.SVC(16)_verbose	false
hyperimp.utils.preprocessing.ConditionalImputer(1)_axis	0
hyperimp.utils.preprocessing.ConditionalImputer(1)_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]
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