Run
10438808

Run 10438808

Task 2079 (Supervised Classification) eucalyptus Uploaded 05-04-2020 by Heinrich Peters
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num =sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standa rdScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing ._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.R andomForestClassifier)(1)Automatically created scikit-learn flow.
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(1)_verbose0
sklearn.preprocessing.data.StandardScaler(29)_copytrue
sklearn.preprocessing.data.StandardScaler(29)_with_meantrue
sklearn.preprocessing.data.StandardScaler(29)_with_stdtrue
sklearn.preprocessing._encoders.OneHotEncoder(11)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(11)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(11)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_sparsetrue
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [false, true, false, false, false, true, true, true, true, false, true, true, true, true, true, true, true, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [true, false, true, true, true, false, false, false, false, true, false, false, false, false, false, false, false, false, false]}}]
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_verbosefalse
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_verbosefalse
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_memorynull
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbosefalse
sklearn.ensemble.forest.RandomForestClassifier(63)_bootstrapfalse
sklearn.ensemble.forest.RandomForestClassifier(63)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(63)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(63)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(63)_max_features0.027087324734051177
sklearn.ensemble.forest.RandomForestClassifier(63)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_decrease0
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_leaf2
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_split7
sklearn.ensemble.forest.RandomForestClassifier(63)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(63)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(63)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(63)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(63)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(63)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(63)_warm_startfalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbosefalse

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
0.8795 ± 0.0245
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6094 ± 0.0563
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.5101 ± 0.071
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3928 ± 0.0281
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2286 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3132 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6264 ± 0.0547
Cross-validation details (10-fold Crossvalidation)
Number of instances
736
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.633 ± 0.0648
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6264 ± 0.0547
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.2621 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7297 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3957 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3226 ± 0.0089
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8153 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5672 ± 0.0584
Cross-validation details (10-fold Crossvalidation)