Run
10591776

Run 10591776

Task 5 (Supervised Classification) arrhythmia Uploaded 11-01-2023 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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotE ncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.Var ianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassif ier)(5)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.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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(5)_memorynull
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(5)_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": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(5)_verbosefalse
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_n_jobsnull
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_sparse_threshold0.3
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_transformer_weightsnull
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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_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.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._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_verbosefalse
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(12)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(12)_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.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(12)_verbosefalse
sklearn.preprocessing.imputation.Imputer(59)_axis0
sklearn.preprocessing.imputation.Imputer(59)_copytrue
sklearn.preprocessing.imputation.Imputer(59)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(59)_strategy"most_frequent"
sklearn.preprocessing.imputation.Imputer(59)_verbose0
sklearn.preprocessing.data.StandardScaler(47)_copytrue
sklearn.preprocessing.data.StandardScaler(47)_with_meantrue
sklearn.preprocessing.data.StandardScaler(47)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_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.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_verbosefalse
sklearn.impute._base.SimpleImputer(37)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(37)_copytrue
sklearn.impute._base.SimpleImputer(37)_fill_value-1
sklearn.impute._base.SimpleImputer(37)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(37)_strategy"constant"
sklearn.impute._base.SimpleImputer(37)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(36)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(36)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(36)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(36)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(36)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(36)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(36)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(35)_threshold0.0
sklearn.tree.tree.DecisionTreeClassifier(72)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(72)_criterion"entropy"
sklearn.tree.tree.DecisionTreeClassifier(72)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(72)_max_features1.0
sklearn.tree.tree.DecisionTreeClassifier(72)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(72)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(72)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(72)_min_samples_leaf14
sklearn.tree.tree.DecisionTreeClassifier(72)_min_samples_split8
sklearn.tree.tree.DecisionTreeClassifier(72)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(72)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(72)_random_state0
sklearn.tree.tree.DecisionTreeClassifier(72)_splitter"best"

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

15 Evaluation measures

0.8137 ± 0.0477
Per class
Cross-validation details (10-fold Crossvalidation)
0.5125 ± 0.0473
Cross-validation details (10-fold Crossvalidation)
0.449 ± 0.0418
Cross-validation details (10-fold Crossvalidation)
0.0531 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
0.0855 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.6792 ± 0.0277
Cross-validation details (10-fold Crossvalidation)
452
Per class
Cross-validation details (10-fold Crossvalidation)
0.6792 ± 0.0277
Cross-validation details (10-fold Crossvalidation)
2.4124 ± 0.0613
Cross-validation details (10-fold Crossvalidation)
0.6207 ± 0.046
Cross-validation details (10-fold Crossvalidation)
0.2055 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
0.1786 ± 0.0097
Cross-validation details (10-fold Crossvalidation)
0.8691 ± 0.0479
Cross-validation details (10-fold Crossvalidation)