Run
10560562

Run 10560562

Task 146822 (Supervised Classification) segment Uploaded 14-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=s klearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imp uter,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=skle arn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotenco der=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier= sklearn.ensemble.forest.RandomForestClassifier)(2)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 to None.
sklearn.preprocessing.imputation.Imputer(56)_axis0
sklearn.preprocessing.imputation.Imputer(56)_copytrue
sklearn.preprocessing.imputation.Imputer(56)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(56)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(56)_verbose0
sklearn.preprocessing.data.StandardScaler(44)_copytrue
sklearn.preprocessing.data.StandardScaler(44)_with_meantrue
sklearn.preprocessing.data.StandardScaler(44)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.impute.SimpleImputer(19)_copytrue
sklearn.impute.SimpleImputer(19)_fill_value-1
sklearn.impute.SimpleImputer(19)_missing_valuesNaN
sklearn.impute.SimpleImputer(19)_strategy"constant"
sklearn.impute.SimpleImputer(19)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(28)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(28)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(28)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_sparsetrue
sklearn.ensemble.forest.RandomForestClassifier(70)_bootstrapfalse
sklearn.ensemble.forest.RandomForestClassifier(70)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(70)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(70)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(70)_max_features0.11661928022424162
sklearn.ensemble.forest.RandomForestClassifier(70)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(70)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(70)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(70)_min_samples_leaf17
sklearn.ensemble.forest.RandomForestClassifier(70)_min_samples_split8
sklearn.ensemble.forest.RandomForestClassifier(70)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(70)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(70)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(70)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(70)_random_state16167
sklearn.ensemble.forest.RandomForestClassifier(70)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(70)_warm_startfalse
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(3)_memorynull
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "missingindicator", "step_name": "missingindicator"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.impute.MissingIndicator(4)_error_on_newfalse
sklearn.impute.MissingIndicator(4)_features"missing-only"
sklearn.impute.MissingIndicator(4)_missing_valuesNaN
sklearn.impute.MissingIndicator(4)_sparse"auto"
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]

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

0.9815 ± 0.0051
Per class
Cross-validation details (10-fold Crossvalidation)
0.8709 ± 0.0225
Per class
Cross-validation details (10-fold Crossvalidation)
0.851 ± 0.0252
Cross-validation details (10-fold Crossvalidation)
0.8213 ± 0.0158
Cross-validation details (10-fold Crossvalidation)
0.0645 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
0.2449
Cross-validation details (10-fold Crossvalidation)
0.8723 ± 0.0216
Cross-validation details (10-fold Crossvalidation)
2310
Per class
Cross-validation details (10-fold Crossvalidation)
0.873 ± 0.0237
Per class
Cross-validation details (10-fold Crossvalidation)
0.8723 ± 0.0216
Cross-validation details (10-fold Crossvalidation)
2.8074
Cross-validation details (10-fold Crossvalidation)
0.2635 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
0.3499
Cross-validation details (10-fold Crossvalidation)
0.1686 ± 0.01
Cross-validation details (10-fold Crossvalidation)
0.4818 ± 0.0287
Cross-validation details (10-fold Crossvalidation)
0.8723 ± 0.0216
Cross-validation details (10-fold Crossvalidation)