https://github.com/trilokida/scikit-learn-pipeline

Utilities to build a composite estimator as a chain of transforms and estimators.
https://github.com/trilokida/scikit-learn-pipeline

Last synced: 4 days ago
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Utilities to build a composite estimator as a chain of transforms and estimators.

• Host: GitHub
• URL: https://github.com/trilokida/scikit-learn-pipeline
• Owner: TrilokiDA
• Created: 2024-08-11T08:10:46.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-12T19:43:04.000Z (3 months ago)
• Last Synced: 2024-08-12T22:48:51.672Z (3 months ago)
• Language: Jupyter Notebook
• Size: 52.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# scikit-learn-pipeline

**Pipeline** can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification. 

Pipeline serves multiple purposes here:

Convenience and encapsulation 

- You only have to call fit and predict once on your data to fit a whole sequence of estimators.

Joint parameter selection 

- You can grid search over parameters of all estimators in the pipeline at once.

Safety

- Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors.

[sklearn.pipeline](https://scikit-learn.org/stable/api/sklearn.pipeline.html)