Run
10560764

Run 10560764

Task 125920 (Supervised Classification) dresses-sales Uploaded 26-08-2021 by Pieter Gijsbers
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  • openml-python Sklearn_0.24.2. study_275
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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_state31861
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

Area under ROC curve
0.5608 ± 0.0598
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6075 ± 0.0619
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1983 ± 0.1257
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0568 ± 0.0355
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4656 ± 0.0125
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4873
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.632 ± 0.0575
Cross-validation details (10-fold Crossvalidation)
Number of instances
500
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6261 ± 0.0721
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.632 ± 0.0575
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9815
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9556 ± 0.0257
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4936
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4831 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9788 ± 0.0297
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5934 ± 0.0598
Cross-validation details (10-fold Crossvalidation)