82 min listen
Build Composable And Reusable Feature Engineering Pipelines with Feature-Engine
Build Composable And Reusable Feature Engineering Pipelines with Feature-Engine
ratings:
Length:
53 minutes
Released:
Oct 31, 2021
Format:
Podcast episode
Description
Every machine learning model has to start with feature engineering. This is the process of combining input variables into a more meaningful signal for the problem that you are trying to solve. Many times this process can lead to duplicating code from previous projects, or introducing technical debt in the form of poorly maintained feature pipelines. In order to make the practice more manageable Soledad Galli created the feature-engine library. In this episode she explains how it has helped her and others build reusable transformations that can be applied in a composable manner with your scikit-learn projects. She also discusses the importance of understanding the data that you are working with and the domain in which your model will be used to ensure that you are selecting the right features.
Released:
Oct 31, 2021
Format:
Podcast episode
Titles in the series (100)
Brian Granger and Fernando Perez of the IPython Project by The Python Podcast.__init__