Run 9200436

Task 35 (Supervised Classification) dermatology Uploaded 06-05-2018 by Benjamin Strang
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeli ne.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one- hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding= sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scal ing=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classe s.SVC))(1)	Automatically created scikit-learn flow.
mylib.preprocessing_openml14.ConditionalImputer(1)_axis	0
mylib.preprocessing_openml14.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, 30, 31, 32]
mylib.preprocessing_openml14.ConditionalImputer(1)_copy	true
mylib.preprocessing_openml14.ConditionalImputer(1)_fill_empty	0
mylib.preprocessing_openml14.ConditionalImputer(1)_missing_values	"NaN"
mylib.preprocessing_openml14.ConditionalImputer(1)_strategy	"median"
mylib.preprocessing_openml14.ConditionalImputer(1)_strategy_nominal	"most_frequent"
mylib.preprocessing_openml14.ConditionalImputer(1)_verbose	0
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, 30, 31, 32]
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	1.0
sklearn.svm.classes.SVC(16)_cache_size	1000
sklearn.svm.classes.SVC(16)_class_weight	null
sklearn.svm.classes.SVC(16)_coef0	0.0
sklearn.svm.classes.SVC(16)_decision_function_shape	"ovr"
sklearn.svm.classes.SVC(16)_degree	3
sklearn.svm.classes.SVC(16)_gamma	"auto"
sklearn.svm.classes.SVC(16)_kernel	"rbf"
sklearn.svm.classes.SVC(16)_max_iter	10000
sklearn.svm.classes.SVC(16)_probability	true
sklearn.svm.classes.SVC(16)_random_state	17253
sklearn.svm.classes.SVC(16)_shrinking	true
sklearn.svm.classes.SVC(16)_tol	0.001
sklearn.svm.classes.SVC(16)_verbose	false
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_cv	3
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_error_score	"raise"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_fit_params	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_iid	true
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_n_iter	250
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_n_jobs	-1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_param_distributions	{"classifier__C": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 0.03125, "b": 32768, "args": [], "kwds": {"base": 2, "low": 0.03125, "high": 32768}}}, "classifier__gamma": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 3.0517578125e-05, "b": 8, "args": [], "kwds": {"base": 2, "low": 3.0517578125e-05, "high": 8}}}, "classifier__shrinking": [true, false], "classifier__tol": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "mylib.distributions.loguniform_gen", "a": 1e-05, "b": 0.1, "args": [], "kwds": {"base": 10, "low": 1e-05, "high": 0.1}}}, "imputation__strategy": ["mean", "median", "most_frequent"]}
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_pre_dispatch	"2*n_jobs"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_random_state	1
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_refit	true
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_return_train_score	"warn"
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_scoring	null
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC))(1)_verbose	0
sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC)(1)_memory	null