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10593874

Run 10593874

Task 3 (Supervised Classification) kr-vs-kp Uploaded 02-07-2023 by Luís Miguel Matos
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Flow

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=skle arn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._bas e.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.pre processing._data.MinMaxScaler)),BayesinClassifier=sklearn.ensemble._hist_gr adient_boosting.gradient_boosting.HistGradientBoostingClassifier)(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.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)),BayesinClassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)),BayesinClassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "BayesinClassifier", "step_name": "BayesinClassifier"}}]
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)),BayesinClassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler))(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler))(2)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler))(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler))(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler))(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler))(2)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler))(2)_verbose_feature_names_outtrue
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(4)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(4)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(45)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(45)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(45)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(45)_handle_unknown"infrequent_if_exist"
sklearn.preprocessing._encoders.OneHotEncoder(45)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(45)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(45)_sparsefalse
sklearn.impute._base.SimpleImputer(49)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(49)_copytrue
sklearn.impute._base.SimpleImputer(49)_fill_valuenull
sklearn.impute._base.SimpleImputer(49)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(49)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(49)_verbose"deprecated"
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)(2)_memorynull
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "minmaxscaler", "step_name": "minmaxscaler"}}]
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)(2)_verbosefalse
sklearn.preprocessing._data.MinMaxScaler(6)_clipfalse
sklearn.preprocessing._data.MinMaxScaler(6)_copytrue
sklearn.preprocessing._data.MinMaxScaler(6)_feature_range[0, 1]
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_categorical_featuresnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_early_stopping"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_l2_regularization0.9188922252522599
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_learning_rate0.9360127059615437
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_loss"log_loss"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_max_bins97
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_max_depthnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_max_iter139
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_max_leaf_nodes79
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_min_samples_leaf47
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_monotonic_cstnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_n_iter_no_change10
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_random_state55168
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_scoring"loss"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_tol0.5967059759761665
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_validation_fraction0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(13)_warm_startfalse

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
0.9998 ± 0.0002
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9947 ± 0.0039
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9893 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9871 ± 0.0083
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0067 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9947 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9947 ± 0.0038
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9947 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0134 ± 0.0084
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0675 ± 0.0321
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.1352 ± 0.0642
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9947 ± 0.0039
Cross-validation details (10-fold Crossvalidation)