Run
10591680

Run 10591680

Task 96 (Learning Curve) diabetes Uploaded 24-10-2022 by Lucas Maertens
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Flow

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncode r,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensembl e._forest.RandomForestClassifier)(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 it to `'passthrough'` or `None`.
sklearn.impute._base.SimpleImputer(34)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(34)_copytrue
sklearn.impute._base.SimpleImputer(34)_fill_valuenull
sklearn.impute._base.SimpleImputer(34)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(34)_strategy"median"
sklearn.impute._base.SimpleImputer(34)_verbose"deprecated"
sklearn.ensemble._forest.RandomForestClassifier(17)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(17)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(17)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(17)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(17)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(17)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(17)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(17)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(17)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(17)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(17)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(17)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(17)_n_estimators10
sklearn.ensemble._forest.RandomForestClassifier(17)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(17)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(17)_random_state46903
sklearn.ensemble._forest.RandomForestClassifier(17)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(17)_warm_startfalse
sklearn.preprocessing._encoders.OneHotEncoder(33)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(33)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(33)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(33)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(33)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(33)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(33)_sparsefalse
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(4)_memorynull
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(4)_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,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)(4)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_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": "continuous", "step_name": "continuous", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer)(4)_verbose_feature_names_outtrue

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.

18 Evaluation measures

0.7957 ± 0.0528
Per class
0.7351 ± 0.0515
Per class
0.4052 ± 0.1174
0.3012 ± 0.073
0.3186 ± 0.0288
0.4545 ± 0.0015
0.7449 ± 0.0474
7680
Per class
0.7372 ± 0.0535
Per class
0.7449 ± 0.0474
0.9331 ± 0.0044
0.7009 ± 0.0637
0.4766 ± 0.0015
0.4166 ± 0.0294
0.874 ± 0.062
0.6912 ± 0.0583