Feature Selection
The process of selecting the most relevant features to improve model performance and reduce complexity.
In more detail
Feature selection methods include filter (statistical), wrapper (model-based), and embedded (during training). Reduces overfitting, improves interpretability, and decreases computation. Important with high-dimensional data.
Examples
- Correlation analysis
- Mutual information
- Recursive feature elimination
- Feature importance from tree models
Related terms
Ready to Build Production AI Systems?
Join a small cohort learning directly from an engineer who's shipped AI at Atlassian and PhonePe scale.
20 weeks · Weekend live classes · 1:1 mentorship · 20+ projects.
20 Weeks
Live Weekend Classes
< 30 Seats
Small Batch
Next Soon
Get priority access
7-Day
Money-Back Guarantee
Taught by Debasish Maji · Senior AI Engineer · Ex-Atlassian (Rovo Agent) · Ex-PhonePe (550M+ users)
Get notified when the next batch opens + free AI resources