OpenML
10593873

Run 10593873

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)),Classifier=sklearn.ensemble._hist_gradient_ 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.impute._base.SimpleImputer(44)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(44)_copytrue
sklearn.impute._base.SimpleImputer(44)_fill_valuenull
sklearn.impute._base.SimpleImputer(44)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(44)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(44)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(42)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(42)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(42)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(42)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(42)_sparsefalse
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)),Classifier=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)),Classifier=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": "Classifier", "step_name": "Classifier"}}]
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)),Classifier=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))(1)_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))(1)_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))(1)_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))(1)_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))(1)_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))(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))(1)_verbose_feature_names_outtrue
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(3)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(3)_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)(3)_verbosefalse
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)(1)_memorynull
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "minmaxscaler", "step_name": "minmaxscaler"}}]
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)(1)_verbosefalse
sklearn.preprocessing._data.MinMaxScaler(5)_clipfalse
sklearn.preprocessing._data.MinMaxScaler(5)_copytrue
sklearn.preprocessing._data.MinMaxScaler(5)_feature_range[0, 1]
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_categorical_featuresnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_early_stopping"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_l2_regularization0.7292700408359999
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_learning_rate0.8417112447267967
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_max_bins145
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_max_depthnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_max_iter189
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_max_leaf_nodes100
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_min_samples_leaf62
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_monotonic_cstnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_n_iter_no_change10
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_random_state19290
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_scoring"loss"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_tol0.37450887524192156
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_validation_fraction0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(12)_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
0.9995 ± 0.0009
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9944 ± 0.0041
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9887 ± 0.0083
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9872 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0067 ± 0.0044
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9944 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9944 ± 0.004
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9944 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0134 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0678 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.1357 ± 0.066
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9944 ± 0.0041
Cross-validation details (10-fold Crossvalidation)