Training and Testing AI
Learn how to train and test AI models properly.
Train AI the right way.
Split Your Data
**Training Data (80%)**: Teach AI **Testing Data (20%)**: Check if AI learned
Like studying with practice questions, then taking the real exam!
Why Split?
If AI sees test data during training, it's cheating!
We want AI to work on NEW data it hasn't seen.
Training Process
1. Show training data 2. AI makes predictions 3. Check if predictions are correct 4. AI adjusts itself 5. Repeat until accurate
Example
Teaching AI to predict house prices in Denver:
**Training**: 800 houses **Testing**: 200 houses
Train on 800, test on 200 new houses.
Overfitting Problem
**Overfitting**: AI memorizes training data but fails on new data
Like memorizing answers instead of understanding concepts!
Solution to Overfitting
- Use more data - Simpler model - Validation techniques
Validation Data
Sometimes split into 3 parts: - Training (60%) - Validation (20%) - Testing (20%)
Measuring Accuracy
**Good**: 95% accuracy on test data **Bad**: 99% on training, 60% on test (overfitting!)
Remember
- Always split data - Test on unseen data - Watch for overfitting