Run
32112

Run 32112

Task 1196 (Supervised Classification) iris Uploaded 30-03-2021 by Continuous Integration
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Flow

sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Varian ceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold, Estimator=sklearn.naive_bayes.GaussianNB)(3)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(17)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(17)_copytrue
sklearn.impute._base.SimpleImputer(17)_fill_valuenull
sklearn.impute._base.SimpleImputer(17)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(17)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(17)_verbose0
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.05
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(3)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(3)_verbosefalse
sklearn.naive_bayes.GaussianNB(11)_priorsnull
sklearn.naive_bayes.GaussianNB(11)_var_smoothing1e-09

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

0.9944 ± 0.0064
Per class
Cross-validation details (10-fold Crossvalidation)
0.96 ± 0.048
Per class
Cross-validation details (10-fold Crossvalidation)
0.94 ± 0.0699
Cross-validation details (10-fold Crossvalidation)
0.93 ± 0.0568
Cross-validation details (10-fold Crossvalidation)
0.0365 ± 0.027
Cross-validation details (10-fold Crossvalidation)
0.4444
Cross-validation details (10-fold Crossvalidation)
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)
150
Per class
Cross-validation details (10-fold Crossvalidation)
0.96 ± 0.0355
Per class
Cross-validation details (10-fold Crossvalidation)
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)
1.585
Cross-validation details (10-fold Crossvalidation)
0.0822 ± 0.0608
Cross-validation details (10-fold Crossvalidation)
0.4714
Cross-validation details (10-fold Crossvalidation)
0.1538 ± 0.0914
Cross-validation details (10-fold Crossvalidation)
0.3262 ± 0.1939
Cross-validation details (10-fold Crossvalidation)
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)