Run
10569632

Run 10569632

Task 3 (Supervised Classification) kr-vs-kp Uploaded 01-12-2021 by Marc Boel
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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),kneighborsclassif ier=sklearn.neighbors._classification.KNeighborsClassifier)(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.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_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)(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbose_feature_names_outtrue
sklearn.impute._base.SimpleImputer(28)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(28)_copytrue
sklearn.impute._base.SimpleImputer(28)_fill_valuenull
sklearn.impute._base.SimpleImputer(28)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(28)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(28)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(30)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(30)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(30)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(30)_sparsetrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "kneighborsclassifier", "step_name": "kneighborsclassifier"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier)(1)_verbosefalse
sklearn.neighbors._classification.KNeighborsClassifier(10)_algorithm"auto"
sklearn.neighbors._classification.KNeighborsClassifier(10)_leaf_size37
sklearn.neighbors._classification.KNeighborsClassifier(10)_metric"minkowski"
sklearn.neighbors._classification.KNeighborsClassifier(10)_metric_paramsnull
sklearn.neighbors._classification.KNeighborsClassifier(10)_n_jobsnull
sklearn.neighbors._classification.KNeighborsClassifier(10)_n_neighbors6
sklearn.neighbors._classification.KNeighborsClassifier(10)_p2.8444786389333268
sklearn.neighbors._classification.KNeighborsClassifier(10)_weights"distance"

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

0.9925 ± 0.005
Per class
Cross-validation details (10-fold Crossvalidation)
0.963 ± 0.0129
Per class
Cross-validation details (10-fold Crossvalidation)
0.9259 ± 0.026
Cross-validation details (10-fold Crossvalidation)
0.7744 ± 0.013
Cross-validation details (10-fold Crossvalidation)
0.1274 ± 0.0059
Cross-validation details (10-fold Crossvalidation)
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
0.9631 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
3196
Per class
Cross-validation details (10-fold Crossvalidation)
0.9636 ± 0.0125
Per class
Cross-validation details (10-fold Crossvalidation)
0.9631 ± 0.0129
Cross-validation details (10-fold Crossvalidation)
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.2554 ± 0.0117
Cross-validation details (10-fold Crossvalidation)
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
0.2086 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
0.4177 ± 0.0242
Cross-validation details (10-fold Crossvalidation)
0.9622 ± 0.0134
Cross-validation details (10-fold Crossvalidation)