TESTde43a98235sklearn.pipeline.Pipeline(ohe=sklearn.preprocessing._encoders.OneHotEncoder,scaler=sklearn.preprocessing._data.StandardScaler,fu=sklearn.pipeline.FeatureUnion(pca=sklearn.decomposition._truncated_svd.TruncatedSVD,fs=sklearn.feature_selection._univariate_selection.SelectPercentile),boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))

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...