Run 9201119

Task 3555 (Supervised Classification) prnn_synth 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.tree.tree. DecisionTreeClassifier))(1)	Automatically created scikit-learn flow.
mylib.preprocessing_openml14.ConditionalImputer(1)_axis	0
mylib.preprocessing_openml14.ConditionalImputer(1)_categorical_features	[]
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	[]
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.tree.tree.DecisionTreeClassifier(18)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(18)_criterion	"gini"
sklearn.tree.tree.DecisionTreeClassifier(18)_max_depth	1
sklearn.tree.tree.DecisionTreeClassifier(18)_max_features	null
sklearn.tree.tree.DecisionTreeClassifier(18)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(18)_min_impurity_decrease	0.0
sklearn.tree.tree.DecisionTreeClassifier(18)_min_impurity_split	null
sklearn.tree.tree.DecisionTreeClassifier(18)_min_samples_leaf	1
sklearn.tree.tree.DecisionTreeClassifier(18)_min_samples_split	2
sklearn.tree.tree.DecisionTreeClassifier(18)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(18)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(18)_random_state	24187
sklearn.tree.tree.DecisionTreeClassifier(18)_splitter	"best"
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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(1)_n_iter	60
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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(1)_param_distributions	{"classifier__criterion": ["gini", "entropy"], "classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], "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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier))(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.tree.tree.DecisionTreeClassifier)(1)_memory	null