Implementing Machine Learninng Pipelines USsing Sklearn And Python
github: https://github.com/krishnaik06/Pipeline-MAchine-Learning Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and ...
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github: https://github.com/krishnaik06/Pipeline-MAchine-Learning
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.
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Sep 1, 2022
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#yt:cc=on #sklearn columntransformer #sklearn pipeline example #sklearn pipeline gridsearchcv #sklearn pipeline example regression #sklearn pipeline tutorial #sklearn pipeline custom transformer #sklearn pipeline train test split #sklearn pipeline multiple classifiers #krish naik machine learning
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