Would You Use ANOVA for Feature Selection?
Last Updated on November 24, 2023 by Editorial Team
Author(s): Sai Viswanth
Originally published on Towards AI.
Know A-Z of ANOVA with an interesting dataset.
Photo by Elimende Inagella on Unsplash
We often forget the most crucial step when developing a Machine learning model — Feature Selection. Not selecting the right features correlated to the target variable can prevent your model from reaching the potential performance.
Feature Selection impacts the entire pipeline in two ways:-
Removes useless and redundant featuresHigh probability of increasing the performance in the worst case no change in accuracy.
Choosing the right technique can help you to converge to the right set of features faster. Sometimes, you do have to find out by iteratively trying out various methods.
Filter methods rely on statistical formulations for ranking… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.