Visibility: public Uploaded 29-11-2021 by Marc Boel sklearn==1.0.1 numpy>=1.14.6 scipy>=1.1.0 joblib>=0.11 threadpoolctl>=2.0.0 15492 runs
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  • openml-python python scikit-learn sklearn sklearn_1.0.1
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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`.


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 the order in which they are chained, with the last object an estimatordefault: [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
verboseIf True, the time elapsed while fitting each step will be printed as it is completed.default: false


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