Run
80267

Run 80267

Task 1 (Supervised Classification) anneal Uploaded 06-07-2020 by Test Test
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Flow

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute ._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder) ),Classifier=sklearn.ensemble.forest.RandomForestClassifier)(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.ensemble.forest.RandomForestClassifier(16)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(16)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(16)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state62792
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_startfalse
sklearn.impute._base.SimpleImputer(7)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(7)_copytrue
sklearn.impute._base.SimpleImputer(7)_fill_valuenull
sklearn.impute._base.SimpleImputer(7)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(7)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(7)_verbose0
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder)),Classifier=sklearn.ensemble.forest.RandomForestClassifier)(5)_memorynull
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder)),Classifier=sklearn.ensemble.forest.RandomForestClassifier)(5)_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(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder)),Classifier=sklearn.ensemble.forest.RandomForestClassifier)(5)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "Nominal", "step_name": "Nominal", "argument_1": [0, 1, 2, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 35, 36, 37]}}]
sklearn.compose._column_transformer.ColumnTransformer(Nominal=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder))(5)_verbosefalse
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder)(5)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Encoder", "step_name": "Encoder"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Encoder=sklearn.preprocessing._encoders.OneHotEncoder)(5)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(5)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(5)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(5)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(5)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(5)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(5)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(5)_sparsefalse

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.

17 Evaluation measures

0.8893 ± 0.0233
Per class
Cross-validation details (10-fold Crossvalidation)
0.824 ± 0.0358
Per class
Cross-validation details (10-fold Crossvalidation)
0.5543 ± 0.1019
Cross-validation details (10-fold Crossvalidation)
0.5451 ± 0.0543
Cross-validation details (10-fold Crossvalidation)
0.0745 ± 0.007
Cross-validation details (10-fold Crossvalidation)
0.1343 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.8385 ± 0.0242
Cross-validation details (10-fold Crossvalidation)
898
Per class
Cross-validation details (10-fold Crossvalidation)
0.8282 ± 0.036
Per class
Cross-validation details (10-fold Crossvalidation)
0.8385 ± 0.0242
Cross-validation details (10-fold Crossvalidation)
1.1915 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
0.555 ± 0.05
Cross-validation details (10-fold Crossvalidation)
0.2582 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
0.1948 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
0.7544 ± 0.0402
Cross-validation details (10-fold Crossvalidation)