Fi
Finding errors in datasets with Similarity Search
Hacker NewsFinding errors in datasets with Similarity Search
The process of splitting or labeling data is error-prone, and these errors can be very costly. George Panchuk, ML Engineer at Qdrant, shares how to define misplaced items with similarity search and avoid errors in categorized datasets.
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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto48%48% predicted probability of success on BetaList, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting14%14% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Correct prediction on native model
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Create dirty datasets out of clean datasets
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