Run
10593877

Run 10593877

Task 3 (Supervised Classification) kr-vs-kp Uploaded 02-07-2023 by Luís Miguel Matos
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer .ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=skle arn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipelin e(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceT hreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimpute r=sklearn.impute._base.SimpleImputer)),BayesinClassifier=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(46)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(46)_copytrue
sklearn.impute._base.SimpleImputer(46)_fill_valuenull
sklearn.impute._base.SimpleImputer(46)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(46)_strategy"mean"
sklearn.impute._base.SimpleImputer(46)_verbose"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(46)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(46)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(46)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(46)_handle_unknown"infrequent_if_exist"
sklearn.preprocessing._encoders.OneHotEncoder(46)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(46)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(46)_sparsefalse
sklearn.preprocessing._data.MinMaxScaler(7)_clipfalse
sklearn.preprocessing._data.MinMaxScaler(7)_copytrue
sklearn.preprocessing._data.MinMaxScaler(7)_feature_range[0, 1]
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_categorical_featuresnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_early_stoppingtrue
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_l2_regularization0.7613033569413896
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_learning_rate0.6358038874192351
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_max_bins27
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_max_depthnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_max_iter388
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_max_leaf_nodes21
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_min_samples_leaf31
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_monotonic_cstnull
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_n_iter_no_change10
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_random_state45005
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_scoring"loss"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_tol0.5291910929988421
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_validation_fraction0.1
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(14)_warm_startfalse
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer)),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),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer)),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),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer)),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),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer))(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer))(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer))(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),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer))(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer))(1)_verbose_feature_names_outtrue
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(14)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(14)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(14)_verbosefalse
sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer)(1)_memorynull
sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "minmaxscaler", "step_name": "minmaxscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}]
sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,simpleimputer=sklearn.impute._base.SimpleImputer)(1)_verbosefalse
sklearn.feature_selection._variance_threshold.VarianceThreshold(11)_threshold0.0

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
0.9996 ± 0.0004
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9925 ± 0.0056
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.985 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9674 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.019 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9925 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9925 ± 0.0055
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9925 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0382 ± 0.0098
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.078 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.1562 ± 0.0469
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9925 ± 0.0056
Cross-validation details (10-fold Crossvalidation)