Understanding Pipeline in Machine Learning with Scikit-learn (sklearn pipeline)

Often in Machine Learning and Data Science, you need to perform a sequence of different transformations of the input data (such as finding a set of features or generating new features) before applying a final estimator. Pipeline gives you a single interface for encapsulating transformers and predictors to simplify the process. Since transformers are usually combined with estimators for preprocessing, using pipeline in scikit-learn can be really useful. To be precise, Pipelines sequentially apply a list of transformers and a final estimator. Therefore, the purpose of the pipeline is to assemble several steps that can be cross-validated while setting different parameters. In this video, we discuss the Python implementation of Pipeline using a polynomial regression example.

#Transformer #Estimator #Pipeline

[kianaliaghat]

thanks for your short, useful introduction!
it helped me a lot

[fabianaltendorfer]

that's a great introduction to pipelines! Thanks

[aszx-tvpq]

HI there, very happy with this channel could you explain a bit simpler what is pipeline part!

[nachoeigu]

I have a big one question: What is the difference of build a Machine Learning application with Pipeline and to build a machine learning application with a OOP technique? I see that it is the same.

[adiver_]

hello
As you have imported polynomial features and transformed the independent variable(X_train) for it be fitted in a polynomial regression then why did you put linearregression() as the estimator in the last tuple of the list?? shouldn't you have use polyfit function or something else?
NOTE: I am a beginner here, so the doubts can be silly.

[rishidixit]

Why are all arrays converted to column matrices while applying sklearn