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Task 36 (Supervised Classification) segment Uploaded 20-10-2018 by Scikit-learn Bot
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.pr eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder )),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT hreshold,svc=sklearn.svm.classes.SVC)(1)	Automatically created scikit-learn flow.
sklearn.preprocessing.imputation.Imputer(29)_axis	0
sklearn.preprocessing.imputation.Imputer(29)_copy	true
sklearn.preprocessing.imputation.Imputer(29)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(29)_strategy	"mean"
sklearn.preprocessing.imputation.Imputer(29)_verbose	0
sklearn.preprocessing.data.StandardScaler(14)_copy	true
sklearn.preprocessing.data.StandardScaler(14)_with_mean	true
sklearn.preprocessing.data.StandardScaler(14)_with_std	true
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory	null
sklearn.impute.SimpleImputer(1)_copy	true
sklearn.impute.SimpleImputer(1)_fill_value	-1
sklearn.impute.SimpleImputer(1)_missing_values	NaN
sklearn.impute.SimpleImputer(1)_strategy	"constant"
sklearn.impute.SimpleImputer(1)_verbose	0
sklearn.preprocessing._encoders.OneHotEncoder(3)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(3)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(3)_n_values	null
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparse	true
sklearn.svm.classes.SVC(22)_C	109.41081389256694
sklearn.svm.classes.SVC(22)_cache_size	200
sklearn.svm.classes.SVC(22)_class_weight	null
sklearn.svm.classes.SVC(22)_coef0	0.8292050617084219
sklearn.svm.classes.SVC(22)_decision_function_shape	"ovr"
sklearn.svm.classes.SVC(22)_degree	3
sklearn.svm.classes.SVC(22)_gamma	4.498870981758283
sklearn.svm.classes.SVC(22)_kernel	"sigmoid"
sklearn.svm.classes.SVC(22)_max_iter	-1
sklearn.svm.classes.SVC(22)_probability	false
sklearn.svm.classes.SVC(22)_random_state	28964
sklearn.svm.classes.SVC(22)_shrinking	true
sklearn.svm.classes.SVC(22)_tol	0.007007196115055329
sklearn.svm.classes.SVC(22)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder	"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weights	null
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memory	null
sklearn.feature_selection.variance_threshold.VarianceThreshold(18)_threshold	0.0
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,svc=sklearn.svm.classes.SVC)(1)_memory	null