Imbalanced Data
A dataset where classes have significantly different numbers of examples, creating training challenges.
In more detail
Imbalanced data can bias models toward majority classes. Solutions include oversampling, undersampling, class weights, SMOTE, and focal loss. Requires careful evaluation using metrics like precision, recall, and F1 rather than accuracy.
Examples
- Medical diagnosis (disease rare)
- Fraud detection (fraud uncommon)
- Anomaly detection
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