Tracking algorithm performance during forward testing is a crucial step in evaluating its real-world effectiveness before deployment.

1. Forward testing refers to running the algorithm on new, unseen data after it has been trained, ensuring that its performance is not merely a result of fitting to historical data. During this phase, key metrics such as accuracy, precision, recall, and computational efficiency are closely monitored to assess how well the algorithm generalizes.

2. To maintain transparency and keep stakeholders informed, we will publish weekly performance figures, providing clear insights into the algorithm's ongoing results. This regular reporting ensures that any discrepancies or signs of overfitting, underfitting, or shifts in data distribution are identified early.

3. By continuously tracking performance and making necessary adjustments based on real-time feedback, we can ensure that the algorithm remains reliable and scalable. Through forward testing and transparent reporting, organizations can make data-driven decisions, ensuring the algorithm consistently delivers actionable, real-world results.

Performance for the Week Ending 01/12/2024