Run
10587878

Run 10587878

Task 3 (Supervised Classification) kr-vs-kp Uploaded 30-06-2022 by Peng YU
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  • openml-python Sklearn_1.2.dev0.
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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)(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.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(4)_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)(4)_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)(4)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_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)(3)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(3)_verbose_feature_names_outtrue
sklearn.impute._base.SimpleImputer(32)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(32)_copytrue
sklearn.impute._base.SimpleImputer(32)_fill_valuenull
sklearn.impute._base.SimpleImputer(32)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(32)_strategy"mean"
sklearn.impute._base.SimpleImputer(32)_verbose"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(32)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(32)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(32)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(32)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(32)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(32)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(32)_sparsetrue
sklearn.tree._classes.DecisionTreeClassifier(27)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(27)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(27)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(27)_max_depth1
sklearn.tree._classes.DecisionTreeClassifier(27)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(27)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(27)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(27)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(27)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(27)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(27)_random_state40872
sklearn.tree._classes.DecisionTreeClassifier(27)_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.

18 Evaluation measures

Area under ROC curve
0.6664 ± 0.0164
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6233 ± 0.0235
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3396 ± 0.0323
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2118 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.397 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6605 ± 0.0171
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8015 ± 0.0058
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6605 ± 0.0171
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7955 ± 0.0196
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4455 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8919 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.675 ± 0.0164
Cross-validation details (10-fold Crossvalidation)