Run
10568799

Run 10568799

Task 3485 (Supervised Classification) scene Uploaded 01-12-2021 by Marc Boel
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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"median"
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"entropy"
sklearn.tree._classes.DecisionTreeClassifier(20)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(20)_max_features0.178396796544006
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_leaf3
sklearn.tree._classes.DecisionTreeClassifier(20)_min_samples_split6
sklearn.tree._classes.DecisionTreeClassifier(20)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_random_state12050
sklearn.tree._classes.DecisionTreeClassifier(20)_splitter"random"
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

Area under ROC curve
0.9571 ± 0.0188
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9684 ± 0.0111
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8924 ± 0.0387
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8426 ± 0.0523
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0362 ± 0.012
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2942 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9684 ± 0.011
Cross-validation details (10-fold Crossvalidation)
Number of instances
2407
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9684 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9684 ± 0.011
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.678 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.123 ± 0.0409
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3834 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1695 ± 0.0272
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4421 ± 0.0712
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9454 ± 0.0264
Cross-validation details (10-fold Crossvalidation)