TEST20150c72basklearn.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(estimator=sklearn.tree._classes.DecisionTreeClassifier))

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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:`~s...