Random Forest on ~100k Polymarket questions — 80% accuracy (text-only)

Summary

• The author, u/No_Syrup_4068, has developed a Random Forest model that predicts the outcome (YES/NO) of prediction market questions on Polymarket with ~80% accuracy, using only the text of the question as input.
• The model uses TF-IDF on word n-grams and simple text features (e.g., keywords, presence of numbers/dates) to achieve this result on a large dataset of ~100,000 questions. The author is now paper trading this model.

• The author's thesis is that the linguistic framing of a prediction market question contains significant predictive power about its eventual outcome, independent of external market data or real-world events.

• Quality assessment: This is well-researched DD (due diligence) from a quantitative perspective. The author provides a clear methodology (Random Forest, TF-IDF), specific metrics (80% accuracy, Brier/logloss), dataset size (~100k questions), and validation steps (held-out set, cross-validation on Kalshi data). The inclusion of a link to a paper trading leaderboard adds a layer of transparency.