Run
10560732

Run 10560732

Task 49 (Supervised Classification) tic-tac-toe Uploaded 26-08-2021 by Pieter Gijsbers
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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)(1)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(25)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(25)_copytrue
sklearn.impute._base.SimpleImputer(25)_fill_valuenull
sklearn.impute._base.SimpleImputer(25)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(25)_strategy"mean"
sklearn.impute._base.SimpleImputer(25)_verbose0
sklearn.tree._classes.DecisionTreeClassifier(20)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(20)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(20)_max_depth1
sklearn.tree._classes.DecisionTreeClassifier(20)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(20)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(20)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_min_impurity_splitnull
sklearn.tree._classes.DecisionTreeClassifier(20)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(20)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(20)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_random_state50545
sklearn.tree._classes.DecisionTreeClassifier(20)_splitter"best"
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_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)(1)_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)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_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)(1)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(29)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(29)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(29)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(29)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(29)_sparsetrue

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

0.6491 ± 0.0407
Per class
Cross-validation details (10-fold Crossvalidation)
0.7002 ± 0.0347
Per class
Cross-validation details (10-fold Crossvalidation)
0.34 ± 0.0793
Cross-validation details (10-fold Crossvalidation)
0.1265 ± 0.0323
Cross-validation details (10-fold Crossvalidation)
0.4008 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
0.453 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.6994 ± 0.034
Cross-validation details (10-fold Crossvalidation)
958
Per class
Cross-validation details (10-fold Crossvalidation)
0.7011 ± 0.0359
Per class
Cross-validation details (10-fold Crossvalidation)
0.6994 ± 0.034
Cross-validation details (10-fold Crossvalidation)
0.931 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
0.8847 ± 0.0229
Cross-validation details (10-fold Crossvalidation)
0.4759 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.4479 ± 0.0134
Cross-validation details (10-fold Crossvalidation)
0.9411 ± 0.0285
Cross-validation details (10-fold Crossvalidation)
0.6709 ± 0.0407
Cross-validation details (10-fold Crossvalidation)