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I can't decipher however the sklearn.pipeline.Pipeline works precisely. Some explanation in this documentation. What is the meaning of Pipeline of transforms with a final estimator. For more clear asking the asking. I am trying to figure out how can I fit a transformer and how can a estimator be a transformer. What happens when I call pipln.fit() or pipln.fit_transform()? How does they work? Here is an example where I am calling pipeline and passing two transformer and one estimator: pipln = Pipeline([("t1",transformer1), ("t2",transformer2), ("est",estimator)]) Select the correct answer from above options

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Transformer are classes which implement both fit() and transform() methods. Classifiers are classes which implement both fit() and predict() methods. APipeline is a series of algorithms chained, composed, and scrambled together in some ways to process a stream of data, it takes inputs and it gives out outputs.ML pipelines mostly have a “fit” and “transform” method. Pipeline allows you to a grid search over a set of parameters for each step of its meta-estimator. Pipeline helps in making a concise code since it encapsulates the predictor and transformer. For example- pipeline = Pipeline([ ('vector', CountVectorizer()), ('trans', TfidfTransformer()), ('cls', SGDClassifier()), ]) prdct = pipeline.fit(Xtrain).predict(Xtrain) prdct = pipeline.predict(Xtest) For your second question, fit(self,X,y=None, **fir_params) Fit() method is called to fit the model. Parameters: X : iterable It fulfills the input requirements of the first step of the pipeline. y : iterable, defauilt=None Training targets. It fulfills label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p. fit_transform(self, X, y=None, **fit_params) It is called to fit the model and transform with the final estimator. Parameters: X : iterable Training data. It fulfills the input requirements of first step of the pipeline. y : iterable, default=None Training targets. It fulfills label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

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