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Task 146817 (Supervised Classification) steel-plates-fault Uploaded 14-04-2018 by Hilde Weerts
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Flow

sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition alImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=skl earn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_se lection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1 )	Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features	[]
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	399.6823015158052
sklearn.svm.classes.SVC(16)_cache_size	200
sklearn.svm.classes.SVC(16)_class_weight	null
sklearn.svm.classes.SVC(16)_coef0	0.9926843699910289
sklearn.svm.classes.SVC(16)_decision_function_shape	"ovr"
sklearn.svm.classes.SVC(16)_degree	3
sklearn.svm.classes.SVC(16)_gamma	0.06104249845810023
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	16486
sklearn.svm.classes.SVC(16)_shrinking	false
sklearn.svm.classes.SVC(16)_tol	2.133017537893654e-05
sklearn.svm.classes.SVC(16)_verbose	false
hyperimp.utils.preprocessing.ConditionalImputer2(1)_axis	0
hyperimp.utils.preprocessing.ConditionalImputer2(1)_categorical_features	[]
hyperimp.utils.preprocessing.ConditionalImputer2(1)_copy	true
hyperimp.utils.preprocessing.ConditionalImputer2(1)_fill_empty	0
hyperimp.utils.preprocessing.ConditionalImputer2(1)_missing_values	"NaN"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_strategy	"mean"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_strategy_nominal	"most_frequent"
hyperimp.utils.preprocessing.ConditionalImputer2(1)_verbose	0
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,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