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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,boosting=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))

Visibility: public Uploaded 29-10-2019 by Continuous Integration sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 5 runs
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  • openml-python python scikit-learn sklearn sklearn_0.21.2 study_6 study_17 study_3170 study_3608 study_3994 study_3996 study_3999 study_4418 study_5311 study_8310 study_8624 study_8625 study_8626 study_9361 study_9363 study_9365 study_11766 study_11768 study_11770 study_11772 study_11774 study_11997 study_12006 study_13178 study_14188 study_14190 study_14194 study_3988
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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``.

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

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