Flow
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))

sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier))

Visibility: public Uploaded 04-07-2024 by Continuous Integration sklearn==1.1.3 numpy>=1.17.3 scipy>=1.3.2 joblib>=1.0.0 threadpoolctl>=2.0.0 5 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_1.1.3
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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`.

Parameters

memoryUsed to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consumingdefault: null
stepsList of (name, transform) tuples (implementing `fit`/`transform`) that are chained in sequential order. The last transform must be an estimatordefault: [{"oml-python:serialized_object": "component_reference", "value": {"key": "enc", "step_name": "enc"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "rs", "step_name": "rs"}}]
verboseIf True, the time elapsed while fitting each step will be printed as it is completed.default: false

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table