Run
10591760

Run 10591760

Task 5 (Supervised Classification) arrhythmia Uploaded 25-11-2022 by Sharath Kumar Reddy Alijarla
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Flow

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,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2 )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))(6)_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.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_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))(6)_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.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_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.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_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)(9)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(9)_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(56)_axis0
sklearn.preprocessing.imputation.Imputer(56)_copytrue
sklearn.preprocessing.imputation.Imputer(56)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(56)_strategy"most_frequent"
sklearn.preprocessing.imputation.Imputer(56)_verbose0
sklearn.preprocessing.data.StandardScaler(44)_copytrue
sklearn.preprocessing.data.StandardScaler(44)_with_meantrue
sklearn.preprocessing.data.StandardScaler(44)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_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(19)_copytrue
sklearn.impute.SimpleImputer(19)_fill_value-1
sklearn.impute.SimpleImputer(19)_missing_valuesNaN
sklearn.impute.SimpleImputer(19)_strategy"constant"
sklearn.impute.SimpleImputer(19)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(28)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(28)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(28)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(32)_threshold0.0
sklearn.tree.tree.DecisionTreeClassifier(69)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(69)_criterion"entropy"
sklearn.tree.tree.DecisionTreeClassifier(69)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(69)_max_features1.0
sklearn.tree.tree.DecisionTreeClassifier(69)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(69)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(69)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(69)_min_samples_leaf3
sklearn.tree.tree.DecisionTreeClassifier(69)_min_samples_split3
sklearn.tree.tree.DecisionTreeClassifier(69)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(69)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(69)_random_state0
sklearn.tree.tree.DecisionTreeClassifier(69)_splitter"best"
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,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_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.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_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"}}]

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

0.7664 ± 0.0329
Per class
Cross-validation details (10-fold Crossvalidation)
0.4436 ± 0.0545
Cross-validation details (10-fold Crossvalidation)
0.4448 ± 0.0431
Cross-validation details (10-fold Crossvalidation)
0.049 ± 0.0047
Cross-validation details (10-fold Crossvalidation)
0.0855 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.6305 ± 0.0407
Cross-validation details (10-fold Crossvalidation)
452
Per class
Cross-validation details (10-fold Crossvalidation)
0.6305 ± 0.0407
Cross-validation details (10-fold Crossvalidation)
2.4124 ± 0.0613
Cross-validation details (10-fold Crossvalidation)
0.5733 ± 0.0552
Cross-validation details (10-fold Crossvalidation)
0.2055 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
0.2005 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
0.9758 ± 0.0537
Cross-validation details (10-fold Crossvalidation)