Run
10560805

Run 10560805

Task 41 (Supervised Classification) soybean Uploaded 11-09-2021 by Victorien Fandos
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Flow

sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer, OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,vc=sklearn.en semble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestCla ssifier,svc=sklearn.svm._classes.SVC))(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(25)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(25)_copytrue
sklearn.impute._base.SimpleImputer(25)_fill_valuenull
sklearn.impute._base.SimpleImputer(25)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(25)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(25)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(11)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(11)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(11)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_depth10
sklearn.ensemble._forest.RandomForestClassifier(11)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(11)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(11)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(11)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(11)_random_state15476
sklearn.ensemble._forest.RandomForestClassifier(11)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(11)_warm_startfalse
sklearn.svm._classes.SVC(9)_C2.85
sklearn.svm._classes.SVC(9)_break_tiesfalse
sklearn.svm._classes.SVC(9)_cache_size200
sklearn.svm._classes.SVC(9)_class_weightnull
sklearn.svm._classes.SVC(9)_coef00.0
sklearn.svm._classes.SVC(9)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(9)_degree3
sklearn.svm._classes.SVC(9)_gamma0.05
sklearn.svm._classes.SVC(9)_kernel"rbf"
sklearn.svm._classes.SVC(9)_max_iter-1
sklearn.svm._classes.SVC(9)_probabilityfalse
sklearn.svm._classes.SVC(9)_random_state52781
sklearn.svm._classes.SVC(9)_shrinkingtrue
sklearn.svm._classes.SVC(9)_tol0.001
sklearn.svm._classes.SVC(9)_verbosefalse
sklearn.preprocessing._encoders.OrdinalEncoder(3)_categories"auto"
sklearn.preprocessing._encoders.OrdinalEncoder(3)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OrdinalEncoder(3)_handle_unknown"error"
sklearn.preprocessing._encoders.OrdinalEncoder(3)_unknown_valuenull
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,vc=sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC))(1)_memorynull
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,vc=sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "SimpleImputer", "step_name": "SimpleImputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "OrdinalEncoder", "step_name": "OrdinalEncoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "vc", "step_name": "vc"}}]
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,vc=sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC))(1)_verbosefalse
sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC)(1)_estimators[{"oml-python:serialized_object": "component_reference", "value": {"key": "rf", "step_name": "rf"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC)(1)_flatten_transformtrue
sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC)(1)_n_jobsnull
sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC)(1)_verbosefalse
sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC)(1)_voting"hard"
sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=sklearn.svm._classes.SVC)(1)_weights[2, 1]

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
0.9681 ± 0.0183
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9435 ± 0.0336
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.939 ± 0.0354
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9523 ± 0.0261
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0059 ± 0.0034
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0961 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9444 ± 0.0325
Cross-validation details (10-fold Crossvalidation)
Number of instances
683
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.949 ± 0.0228
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9444 ± 0.0325
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.8358 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0609 ± 0.0355
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2191 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0765 ± 0.0216
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3493 ± 0.0986
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9719 ± 0.016
Cross-validation details (10-fold Crossvalidation)