My Fall-Detection Model Scored 94%, and It Was Lying to Me
Machine Learning My Fall-Detection Model Scored 94%, and It Was Lying to Me How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on Ramandeep Singh Aug 7, 2026 10 min read Share My fall-detection model scored 94.3% accuracy. I went back over the terminal output and the confusion matrix more than once. The number went into my README and onto my CV.
- ▪Machine Learning My Fall-Detection Model Scored 94%, and It Was Lying to Me How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on Ramandeep Singh Aug
- ▪I went back over the terminal output and the confusion matrix more than once.
- ▪The number went into my README and onto my CV.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/my-fall-detection-model-scored-94-and-it-was-lying-to-me/ |
| Publication time | Fri, 07 Aug 2026 12:00:00 +0000 |
| Retrieval time | 2026-08-07T12:10:41.815Z |
| Last seen | 2026-08-07T12:10:41.815Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | v99qNv-zUHZ4 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Machine Learning My Fall-Detection Model Scored 94%, and It Was Lying to Me How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on Ramandeep Singh Aug 7, 2026 10 min read Share My fall-detection model scored 94.3% accuracy. I went back over the terminal output and the confusion matrix more than once. They agreed every time. The number went into my README and onto my CV. It was also wrong. Not by a rounding error, either. The honest figure was 69%. No one caught this for me. It only came to light because I decided to build tools that could verify my own results. Fixing it taught me more about production machine learning than building the original system did.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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