| Issue | #Downvotes for this reason | By |
|---|
| TESTb5ba83bb0esklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data. StandardScaler,dummy=sklearn.dummy.DummyClassifier)(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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl... |
| TESTb5ba83bb0esklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_memory | null |
| TESTb5ba83bb0esklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "dummy", "step_name": "dummy"}}] |
| TESTb5ba83bb0esklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_verbose | false |
| TESTb5ba83bb0esklearn.preprocessing._data.StandardScaler(1)_copy | true |
| TESTb5ba83bb0esklearn.preprocessing._data.StandardScaler(1)_with_mean | false |
| TESTb5ba83bb0esklearn.preprocessing._data.StandardScaler(1)_with_std | true |
| TESTb5ba83bb0esklearn.dummy.DummyClassifier(1)_constant | null |
| TESTb5ba83bb0esklearn.dummy.DummyClassifier(1)_random_state | 62501 |
| TESTb5ba83bb0esklearn.dummy.DummyClassifier(1)_strategy | "prior" |
0.5 Per class |
0.0048 |
0.4568 |
0.4589 |
0.6364 |
253 Per class |
0.6364 |
0.9463 |
0.9955 |
0.4813 |
0.4815 |
1.0006 |
0.5 |