juniorCost Function
Why do we minimize cost functions?
Updated May 15, 2026
Short answer
Minimizing cost improves model accuracy by reducing prediction errors.
Deep explanation
Optimization algorithms like gradient descent adjust model parameters to minimize cost, leading to better generalization on unseen data.
Real-world example
Training a recommendation system to reduce prediction error in movie ratings.
Common mistakes
- Thinking lower training cost always means better real-world performance.
Follow-up questions
- Can cost reach zero?
- What is overfitting?