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Task 11 (Supervised Classification) balance-scale Uploaded 05-04-2017 by Jeroen van Hoof
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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,stand ardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes .SVC)(1)	Automatically created scikit-learn flow.
sklearn.svm.classes.SVC(5)_C	1000
sklearn.svm.classes.SVC(5)_cache_size	200
sklearn.svm.classes.SVC(5)_class_weight	None
sklearn.svm.classes.SVC(5)_coef0	0.0
sklearn.svm.classes.SVC(5)_decision_function_shape	None
sklearn.svm.classes.SVC(5)_degree	3
sklearn.svm.classes.SVC(5)_gamma	0.03162277660168379
sklearn.svm.classes.SVC(5)_kernel	rbf
sklearn.svm.classes.SVC(5)_max_iter	-1
sklearn.svm.classes.SVC(5)_probability	True
sklearn.svm.classes.SVC(5)_random_state	3
sklearn.svm.classes.SVC(5)_shrinking	True
sklearn.svm.classes.SVC(5)_tol	0.001
sklearn.svm.classes.SVC(5)_verbose	False
sklearn.preprocessing.data.StandardScaler(1)_copy	True
sklearn.preprocessing.data.StandardScaler(1)_with_mean	True
sklearn.preprocessing.data.StandardScaler(1)_with_std	True
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC)(1)_steps	[('dualimputer', ), ('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1000, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma=0.03162277660168379, kernel='rbf', max_iter=-1, probability=True, random_state=3, shrinking=True, tol=0.001, verbose=False))]