evaluation-ml

To calibrate machine learning scores, this skill applies Platt scaling to ensure model predictions are well-calibrated. It automates this process for highlight detection systems, integrating directly into production pipelines to maintain prediction reliability.

Evaluating highlight detection models uses metrics like Precision@K, NDCG, Brier score, and ECE. These metrics assess ranking quality and calibration, helping you monitor model performance and identify areas for continuous improvement.

Training a logistic regression model for highlight detection requires labeled clip data to learn optimal weights and synergy boosts. You need at least 50 samples for gaming content to retrain the model and improve accuracy for future content.

Yes, this model evaluation skill integrates with production pipelines for continuous improvement. It automates training, calibration, and evaluation, allowing you to retrain models with user feedback and monitor performance over time.

Highlight detection models need score calibration to ensure prediction scores accurately reflect true probabilities. This skill uses Platt scaling to fix miscalibrated scores, improving scoring reliability for highlight detection systems.