01. Introduction to Evaluation

Evaluating AI Model Performance

Effectively optimizing AI strategies requires well-defined evaluation metrics. These metrics ensure efforts are directed toward the right improvements by gauging performance accurately.

Steps for Evaluation:

1. Cross-Validation:

   • Test models on multiple validation sets.
   • Train final model on full training set.
   • Evaluate on test set to predict real-world performance.

2. Optimization and Evaluation:

   • Optimization involves adjusting parameters.
   • Evaluation metrics provide feedback on these adjustments.

3. Understanding Baselines:

   • Comparison is crucial; set a basic model (baseline) as a benchmark.
   • It should be simple and computationally efficient.
   • Common baselines include heuristic or statistical models.

Considerations for AI Models in Trading:

• Performance Metrics:
  • Choose suitable metrics to avoid poor decisions.
• Baselines:
  • Use strategies like 'buy and hold' for comparison.
• Feature Selection and Monitoring:
  • Highlight feature selection based on results.
  • Emphasize continuous monitoring for robustness.

Grasping evaluation metrics and using appropriate baselines form the foundation for developing successful AI models.