Run 10593843

Task 14952 (Supervised Classification) PhishingWebsites Uploaded 28-06-2023 by Luís Miguel Matos
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncode r,numeric=sklearn.preprocessing._data.StandardScaler),Classifier=sklearn.en semble._weight_boosting.AdaBoostClassifier)(1)	Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a `'__'`, as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to `'passthrough'` or `None`.
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_algorithm	"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_base_estimator	"deprecated"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_estimator	null
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_learning_rate	0.339740616036483
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_n_estimators	178
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_random_state	30108
sklearn.preprocessing._encoders.OneHotEncoder(43)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(43)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(43)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(43)_handle_unknown	"infrequent_if_exist"
sklearn.preprocessing._encoders.OneHotEncoder(43)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(43)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(43)_sparse	false
sklearn.preprocessing._encoders.OneHotEncoder(43)_sparse_output	true
sklearn.preprocessing._data.StandardScaler(15)_copy	true
sklearn.preprocessing._data.StandardScaler(15)_with_mean	true
sklearn.preprocessing._data.StandardScaler(15)_with_std	true
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler)(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler)(1)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler)(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler)(1)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler)(1)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler)(1)_verbose_feature_names_out	true
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler),Classifier=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler),Classifier=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Classifier", "step_name": "Classifier"}}]
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler),Classifier=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbose	false