Implementing Machine Learninng Pipelines USsing Sklearn And Python

github: 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. ------------------------------------------------------------------------------------------------------------- All Playlist in my channel Github Tutorials : Live NLP Playlist: Live Deep LEarning Playlist: Live EDA Playlist: Live ML Playlist: Live Stats Playlist: My SQL Playlist: --------------------------------------------------------------------------------------------------------------- Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06@okicici Telegram link: ------------------------------------------------------------------------------------------------------------- Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more ----------------------------------------------------------------------------------------------------------- Please do subscribe my other channel too --------------------------------------------------------------------------------------------------------- Connect with me here: Twitter: Facebook: instagram: