Run 10591768

Task 5 (Supervised Classification) arrhythmia Uploaded 23-12-2022 by Sharath Kumar Reddy Alijarla
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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,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassif ier)(4)	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 to None.
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))(7)_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))(7)_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))(7)_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))(7)_transformer_weights	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))(7)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 27, 28, 29, 30, 31, 32, 39, 40, 41, 42, 43, 44, 51, 52, 53, 54, 55, 56, 63, 64, 65, 66, 67, 68, 75, 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104, 111, 112, 113, 114, 115, 116, 123, 124, 125, 126, 127, 128, 135, 136, 137, 138, 139, 140, 147, 148, 149, 150, 151, 152, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [1, 21, 22, 23, 24, 25, 26, 33, 34, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 57, 58, 59, 60, 61, 62, 69, 70, 71, 72, 73, 74, 81, 82, 83, 84, 85, 86, 93, 94, 95, 96, 97, 98, 105, 106, 107, 108, 109, 110, 117, 118, 119, 120, 121, 122, 129, 130, 131, 132, 133, 134, 141, 142, 143, 144, 145, 146, 153, 154, 155, 156, 157, 158]}}]
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(10)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(10)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.preprocessing.imputation.Imputer(57)_axis	0
sklearn.preprocessing.imputation.Imputer(57)_copy	true
sklearn.preprocessing.imputation.Imputer(57)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(57)_strategy	"median"
sklearn.preprocessing.imputation.Imputer(57)_verbose	0
sklearn.preprocessing.data.StandardScaler(45)_copy	true
sklearn.preprocessing.data.StandardScaler(45)_with_mean	true
sklearn.preprocessing.data.StandardScaler(45)_with_std	true
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_memory	null
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.impute.SimpleImputer(21)_copy	true
sklearn.impute.SimpleImputer(21)_fill_value	-1
sklearn.impute.SimpleImputer(21)_missing_values	NaN
sklearn.impute.SimpleImputer(21)_strategy	"constant"
sklearn.impute.SimpleImputer(21)_verbose	0
sklearn.preprocessing._encoders.OneHotEncoder(34)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(34)_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(34)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(34)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(34)_n_values	null
sklearn.preprocessing._encoders.OneHotEncoder(34)_sparse	true
sklearn.feature_selection.variance_threshold.VarianceThreshold(33)_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,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(4)_memory	null
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,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(4)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.ensemble.forest.RandomForestClassifier(71)_bootstrap	true
sklearn.ensemble.forest.RandomForestClassifier(71)_class_weight	null
sklearn.ensemble.forest.RandomForestClassifier(71)_criterion	"entropy"
sklearn.ensemble.forest.RandomForestClassifier(71)_max_depth	null
sklearn.ensemble.forest.RandomForestClassifier(71)_max_features	0.03630592737482119
sklearn.ensemble.forest.RandomForestClassifier(71)_max_leaf_nodes	null
sklearn.ensemble.forest.RandomForestClassifier(71)_min_impurity_decrease	0.0
sklearn.ensemble.forest.RandomForestClassifier(71)_min_impurity_split	null
sklearn.ensemble.forest.RandomForestClassifier(71)_min_samples_leaf	6
sklearn.ensemble.forest.RandomForestClassifier(71)_min_samples_split	13
sklearn.ensemble.forest.RandomForestClassifier(71)_min_weight_fraction_leaf	0.0
sklearn.ensemble.forest.RandomForestClassifier(71)_n_estimators	100
sklearn.ensemble.forest.RandomForestClassifier(71)_n_jobs	null
sklearn.ensemble.forest.RandomForestClassifier(71)_oob_score	false
sklearn.ensemble.forest.RandomForestClassifier(71)_random_state	0
sklearn.ensemble.forest.RandomForestClassifier(71)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(71)_warm_start	false