05. Demo 2: Hyperparameter Tuning with Random Search

Hyperparameter Tuning with Random Search

Hyperparameter tuning is critical for optimizing machine learning models. This shootout compares random search and grid search for tuning parameters in a support vector machine model.

• Setup:

  • Uses a synthetic dataset and support vector machine (SVM).
  • Grid search explored 24 combinations, using six values for the C hyperparameter and four kernel types.
  • Results from the grid search are saved for comparison.

• Random Search Approach:

  • Instead of trying every combination exhaustively, random search selects random values to explore.
  • Focuses on: C value between 1 and 500 using log-uniform distribution, and kernel choice using discrete uniform distribution.
  • Samples the search space 10 times, a significant reduction from 24 trials in grid search.

• Performance Comparisons:

  • Random search discovers equally good or better parameter settings in just 30 seconds compared to grid search's three minutes.
  • Highlights the efficiency of random search, especially for large search spaces.

Random search is recommended initially, reserving grid search for finer tuning if necessary.