Pipelines in Scikit-Learn: An Amazing Way to Bundle Transformations

Overview of Your Journey

1. Introduction
2. Problems Without Pipelines?
3. Pipelines to the Rescue!
4. Useful Attributes and Utility Functions
5. Wrapping Up

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1 – Introduction

One of the most popular Python libraries for dealing with machine learning tasks is scikit-learn. It went public in 2010 and has since been essential for implementing popular supervised ML algorithms like logistic regression, random forests, and support vector machines.

When writing code in scikit-learn, you can use a feature called pipelines. This feature allows you to bundle up several of the steps in the machine learning process into a single component. The use of pipelines is one of the single most determining factors for whether scikit-learn code is easy to work with. It's frustrating how many neglect pipelines when creating machine learning models in scikit-learn

Tags: Code Quality Data Science Machine Learning Python Scikit Learn

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