Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Aug 7·5 min read
My Fall-Detection Model Scored 94%, and It Was Lying to Me
TL;DR
A fall-detection model initially reported a 94% accuracy, but a flawed evaluation method inflated this score by 25 points. Rebuilding the model revealed critical insights about the reliability of machine learning systems.
✦ Why It Matters
Ensure your model evaluation methods are robust to avoid misleading performance metrics.
Key Takeaways
- Group data splits by dependent variables like video or subject.
- Cross-reference labels with actual annotations to ensure accuracy.
- Evaluate models based on user experience and real-world scenarios.
- Document and publish corrections to maintain trust in your system.
How It Works
The fall detection system uses a webcam to capture video, which is processed through MediaPipe Pose to extract skeletal landmarks. These landmarks are analyzed to compute biomechanical features, which are then classified by a Random Forest model.
The model operates in real-time, ensuring that no video data leaves the device, thus maintaining user privacy.
⚠ The Catch
The initial evaluation method allowed for data leakage, as frames from the same video were not independent, leading to inflated accuracy metrics.