Run
10560802

Run 10560802

Task 41 (Supervised Classification) soybean Uploaded 10-09-2021 by Victorien Fandos
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Flow

sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer, OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder,rf=sklearn.ense mble._forest.RandomForestClassifier)(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.preprocessing._encoders.OneHotEncoder(29)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(29)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(29)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(29)_handle_unknown"error"
sklearn.preprocessing._encoders.OneHotEncoder(29)_sparsetrue
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"entropy"
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_estimators800
sklearn.ensemble._forest.RandomForestClassifier(11)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(11)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(11)_random_state36528
sklearn.ensemble._forest.RandomForestClassifier(11)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(11)_warm_startfalse
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder,rf=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder,rf=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": "OneHotEncoder", "step_name": "OneHotEncoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "rf", "step_name": "rf"}}]
sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder,rf=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
0.9967 ± 0.0022
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9395 ± 0.0321
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9342 ± 0.0336
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9175 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0158 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0961 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.94 ± 0.0307
Cross-validation details (10-fold Crossvalidation)
Number of instances
683
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9473 ± 0.0206
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.94 ± 0.0307
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.8358 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1648 ± 0.0231
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
0.0738 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3369 ± 0.041
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9681 ± 0.0224
Cross-validation details (10-fold Crossvalidation)