Evaluation of traditional and novel feature selection approaches
Who is this presentation for?
- Data scientists and data scientist managers
A central component to the machine learning process is feature selection. Selecting the optimal set of features is important to generate a best fit model that generalizes to unseen data. A widely used approach for feature selection involves calculating Gini Importance (Gain) to identify the best set of features.
However, recent work from Scott Lundberg has found challenges with the consistency of the Gain attribution method. Ben Fowler shares the results of model metrics on the Lending Club dataset, testing five different feature selection approaches. The approaches tested involved widely used approaches combined with novel approaches for feature selection.
You’ll discover the impact of the data splitting method, including relevant two-way and three-way interactions (xgbfir library), backwards stepwise feature selection as opposed to a singular feature selection step, and backwards stepwise feature selection using Shapley values (shap library).
- A high-level understanding of the modeling process
What you'll learn
- Gain added predictive power and velocity to the feature selection process
Southeast Toyota Finance
Ben Fowler is a machine learning technical leader at Southeast Toyota Finance, where he leads the end-to-end model development. He’s been in the field of data science for over five years. Ben has been a guest speaker at the Southern Methodist University program multiple times. Additionally, he’s spoken at the PyData Miami 2019 Conference and has spoken multiple times at the West Palm Beach Data Science Meetup. He earned a master of science in data science from Southern Methodist University.
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