| Issue | #Downvotes for this reason | By |
|---|
| 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 |
0.8511 ± 0.0199 Per class Cross-validation details (10-fold Crossvalidation)
|
0.7916 ± 0.0307 Per class Cross-validation details (10-fold Crossvalidation)
|
0.597 ± 0.0458 Cross-validation details (10-fold Crossvalidation)
|
0.5839 ± 0.0528 Cross-validation details (10-fold Crossvalidation)
|
0.1792 ± 0.025 Cross-validation details (10-fold Crossvalidation)
|
0.3798 ± 0.0012 Cross-validation details (10-fold Crossvalidation)
|
0.7312 ± 0.0376 Cross-validation details (10-fold Crossvalidation)
|
625 Per class Cross-validation details (10-fold Crossvalidation) |
0.9278 ± 0.0134 Per class Cross-validation details (10-fold Crossvalidation)
|
0.7312 ± 0.0376 Cross-validation details (10-fold Crossvalidation)
|
1.3181 ± 0.0124 Cross-validation details (10-fold Crossvalidation)
|
0.4719 ± 0.0658 Cross-validation details (10-fold Crossvalidation)
|
0.4356 ± 0.0014 Cross-validation details (10-fold Crossvalidation)
|
0.4233 ± 0.0293 Cross-validation details (10-fold Crossvalidation)
|
0.9719 ± 0.0671 Cross-validation details (10-fold Crossvalidation)
|
0.7886 ± 0.0364 Cross-validation details (10-fold Crossvalidation)
|