Run
10594132

Run 10594132

Task 3549 (Supervised Classification) analcatdata_authorship Uploaded 03-02-2024 by Jano P
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,one hotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclass ifier=sklearn.tree._classes.DecisionTreeClassifier)(9)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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
sklearn.preprocessing._encoders.OneHotEncoder(50)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(50)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(50)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(50)_feature_name_combiner"concat"
sklearn.preprocessing._encoders.OneHotEncoder(50)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(50)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(50)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparse"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparse_outputtrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(9)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(9)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(9)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(8)_verbose_feature_names_outtrue
sklearn.impute._base.SimpleImputer(54)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(54)_copytrue
sklearn.impute._base.SimpleImputer(54)_fill_valuenull
sklearn.impute._base.SimpleImputer(54)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(54)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(54)_strategy"mean"
sklearn.tree._classes.DecisionTreeClassifier(45)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(45)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(45)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(45)_max_depth1
sklearn.tree._classes.DecisionTreeClassifier(45)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(45)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(45)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(45)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(45)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(45)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(45)_random_state21256
sklearn.tree._classes.DecisionTreeClassifier(45)_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.

16 Evaluation measures

0.773 ± 0.0246
Per class
Cross-validation details (10-fold Crossvalidation)
0.4341 ± 0.0554
Cross-validation details (10-fold Crossvalidation)
0.3574 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
0.2447 ± 0.0082
Cross-validation details (10-fold Crossvalidation)
0.3439 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.6421 ± 0.035
Cross-validation details (10-fold Crossvalidation)
841
Per class
Cross-validation details (10-fold Crossvalidation)
0.6421 ± 0.035
Cross-validation details (10-fold Crossvalidation)
1.7874 ± 0.0186
Cross-validation details (10-fold Crossvalidation)
0.7116 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
0.4146 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
0.355 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
0.8562 ± 0.03
Cross-validation details (10-fold Crossvalidation)
0.44 ± 0.0238
Cross-validation details (10-fold Crossvalidation)