Training and Testing AI

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