Скачать или смотреть Turning “Probabilities" Into Decisions?

Ever been frustrated trying to interpret your ML model's confidence scores?

In this practical video, I reveal how to handle the annoying reality of confidence scores in machine learning—when your model acts "just like a politician in a news interview" instead of providing clear yes/no predictions.
Using real examples from my pedestrian detection project, I break down the critical process of transforming probabilistic outputs (like "78% confident this is a pedestrian") into actionable binary decisions. You'll learn the essential concept of confidence thresholds and why choosing the right threshold value is more complex than most tutorials admit
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I explain why there's no universal "correct" threshold—it's entirely dependent on your specific use case and requirements. While this might not be the satisfying one-size-fits-all answer you hoped for, understanding this reality is what separates theoretical knowledge from practical ML implementation.
Perfect for data science students, STEM graduates transitioning to ML roles, and professionals building real-world machine learning systems where decisions must be made despite uncertainty. Master this concept to move beyond toy problems and build models that perform in production environments.

Watch for my upcoming video where I'll dive deeper into different methods for optimizing threshold selection based on your specific needs!

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