(**kwargs): Set the parameters of this estimator
(Xt): Apply for each step in a reverse order
What is the purpose of a data pipeline?
Data pipelines are used to perform data integration. Data integration is the process of bringing together data from multiple sources to provide a complete and accurate dataset for business intelligence (BI), data analysis and other applications and business processes.
Why do we use pipeline in machine learning?
One definition of an ML pipeline is a means of automating the machine learning workflow by enabling data to be transformed and correlated into a model that can then be analyzed to achieve outputs. This type of ML pipeline makes the process of inputting data into the ML model fully automated.
Why do we use pipeline in Python?
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.