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With increasing demand in machine learning and data science in businesses, for upgraded data strategizing there’s a need for a better workflow to ensure robustness in data modelling. Machine learning has certain steps to be followed namely – data collection, data preprocessing(cleaning and feature engineering), model training, validation and prediction on the test data(which is previously unseen by model).
Here testing data needs to go through the same preprocessing as training data. For this iterative process, pipelines are used which can automate the entire process for both training and testing data. It ensures reusability of the model by reducing the redundant part, thereby speeding up the process. This could prove to be very effective during the production workflow.
(Source: YouTube – Pydata )
In this article, I’ll be discussing how to implement a machine learning pipeline using scikit-learn.
Advantages of using Pipeline:
Automating the workflow being iterative.Easier to fix bugs Production ReadyClean code writing standardsHelpful in iterative hyperparameter tuning and cross-validation evaluation
Challenges in using Pipeline:
Proper data cleaningData Exploration and AnalysisEfficient feature engineering
The sklearn.pipeline module implements utilities to build a composite estimator, as a chain of transforms and estimators.
I’ve used the …
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