| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.GenericUnivariateSelect,step_1=sklearn.naive_bayes.BernoulliNB)(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.naive_bayes.BernoulliNB(11)_alpha | 56.03728813137365 |
| sklearn.naive_bayes.BernoulliNB(11)_binarize | 0.0 |
| sklearn.naive_bayes.BernoulliNB(11)_class_prior | null |
| sklearn.naive_bayes.BernoulliNB(11)_fit_prior | false |
| sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_mode | "fdr" |
| sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_param | 0.41665016240906816 |
| sklearn.feature_selection._univariate_selection.GenericUnivariateSelect(1)_score_func | {"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.f_classif"} |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.naive_bayes.BernoulliNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.naive_bayes.BernoulliNB)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}] |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.naive_bayes.BernoulliNB)(1)_verbose | false |
0.8818 ± 0.0197 Per class Cross-validation details (10-fold Crossvalidation)
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0.7949 ± 0.0204 Per class Cross-validation details (10-fold Crossvalidation)
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0.5903 ± 0.0406 Cross-validation details (10-fold Crossvalidation)
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0.588 ± 0.0408 Cross-validation details (10-fold Crossvalidation)
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0.2062 ± 0.0204 Cross-validation details (10-fold Crossvalidation)
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0.4999 ± 0 Cross-validation details (10-fold Crossvalidation)
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0.7956 ± 0.0202 Cross-validation details (10-fold Crossvalidation)
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3468 Per class Cross-validation details (10-fold Crossvalidation) |
0.7981 ± 0.0205 Per class Cross-validation details (10-fold Crossvalidation)
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0.7956 ± 0.0202 Cross-validation details (10-fold Crossvalidation)
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0.9998 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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0.4125 ± 0.0408 Cross-validation details (10-fold Crossvalidation)
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0.4999 ± 0 Cross-validation details (10-fold Crossvalidation)
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0.4452 ± 0.0234 Cross-validation details (10-fold Crossvalidation)
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0.8905 ± 0.0468 Cross-validation details (10-fold Crossvalidation)
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0.7947 ± 0.0203 Cross-validation details (10-fold Crossvalidation)
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