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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)

sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier)

Visibility: public Uploaded 24-02-2021 by Test Test sklearn==0.24.1 numpy>=1.6.1 scipy>=0.9 213 runs
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  • openml-python python scikit-learn sklearn sklearn_0.24.1 study_819 study_838 study_844 study_847 study_927 study_968 study_991 study_994 study_998 study_1001 study_1003 study_1007 study_1008 study_1012 study_1015 study_1019 study_1021 study_1023 study_1027 study_1031 study_1032 study_1033 study_1040 study_1041 study_1045 study_1048 study_1051 study_1054 study_1057 study_1060 study_1069 study_1072 study_1077 study_1080 study_1083 study_1086 study_1089 study_1091 study_1097 study_1098
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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": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Classifier", "step_name": "Classifier"}}]
verboseIf True, the time elapsed while fitting each step will be printed as it is completed.default: false

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