| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.neighbors._nearest_centroid.NearestCentroid)(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(43)_add_indicator | false |
| sklearn.impute._base.SimpleImputer(43)_copy | true |
| sklearn.impute._base.SimpleImputer(43)_fill_value | null |
| sklearn.impute._base.SimpleImputer(43)_keep_empty_features | false |
| sklearn.impute._base.SimpleImputer(43)_missing_values | NaN |
| sklearn.impute._base.SimpleImputer(43)_strategy | "mean" |
| sklearn.impute._base.SimpleImputer(43)_verbose | "deprecated" |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._nearest_centroid.NearestCentroid)(1)_memory | null |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._nearest_centroid.NearestCentroid)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}] |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._nearest_centroid.NearestCentroid)(1)_verbose | false |
| sklearn.neighbors._nearest_centroid.NearestCentroid(1)_metric | "euclidean" |
| sklearn.neighbors._nearest_centroid.NearestCentroid(1)_shrink_threshold | null |
0.7514 ± 0.0053 Per class Cross-validation details (5 times 2-fold Crossvalidation)
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0.7498 ± 0.0043 Per class Cross-validation details (5 times 2-fold Crossvalidation)
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0.4444 ± 0.0089 Cross-validation details (5 times 2-fold Crossvalidation)
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0.3225 ± 0.0118 Cross-validation details (5 times 2-fold Crossvalidation)
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0.2607 ± 0.0045 Cross-validation details (5 times 2-fold Crossvalidation)
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0.4147 ± 0 Cross-validation details (5 times 2-fold Crossvalidation)
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0.7393 ± 0.0045 Cross-validation details (5 times 2-fold Crossvalidation)
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27020 Per class Cross-validation details (5 times 2-fold Crossvalidation) |
0.7854 ± 0.0044 Per class Cross-validation details (5 times 2-fold Crossvalidation)
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0.7393 ± 0.0045 Cross-validation details (5 times 2-fold Crossvalidation)
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0.8732 ± 0 Cross-validation details (5 times 2-fold Crossvalidation)
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0.6285 ± 0.011 Cross-validation details (5 times 2-fold Crossvalidation)
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0.4554 ± 0 Cross-validation details (5 times 2-fold Crossvalidation)
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0.5105 ± 0.0045 Cross-validation details (5 times 2-fold Crossvalidation)
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1.1212 ± 0.0098 Cross-validation details (5 times 2-fold Crossvalidation)
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0.7514 ± 0.0053 Cross-validation details (5 times 2-fold Crossvalidation)
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