Hyperparameter Tuning: Finding Optimal Settings

Hyperparameters are settings you choose before training. Tuning finds the best combination for your data.

Hyperparameters vs Parameters

Parameters: Model learns these (weights, coefficients)

Hyperparameters: You set these (learning rate, tree depth, number of layers)

Common Hyperparameters

Random Forest:

• Number of trees
• Max depth
• Min samples per leaf

Neural Networks:

• Learning rate
• Number of layers
• Batch size

Tuning Methods

Grid Search: Try every combination

✅ Finds best settings
❌ Slow (exponential with parameters)

Random Search: Try random combinations

✅ Faster than grid search
✅ Often finds good-enough settings

Bayesian Optimization: Smart search using past results

✅ Most efficient
❌ More complex setup

Practical Advice

1. Start with defaults
2. If performance isn't good enough, tune the 2-3 most important parameters
3. Use Random Search first (faster)
4. Only do full Grid Search if you have time and compute