poker44-my-model

poker44-my-model (top_blend)

Original bot-detection model for Poker44 (Bittensor subnet 126).

Model description

top_blend is a weighted-average ensemble (60/40) of two independently trained variants:

• rank1_gbm — LightGBM gradient-boosted classifier over chunk-level behavioral features
• rank3_stacked — out-of-fold stacked ensemble (ExtraTrees + RandomForest
  • HistGradientBoosting + LightGBM) with a logistic-regression meta-learner

Both members apply isotonic calibration and a conformal false-positive-rate safety shift on top of the raw model score, matching the validator's threshold-based scoring at 0.5.

Training data

Public Poker44 training benchmark (https://api.poker44.net/api/v1/benchmark), 15 release dates (2026-07-01 through 2026-07-15). No private or validator-only evaluation data was used.

Evaluation

Leave-one-date-out cross-validation using the validator's exact reward() formula (poker44/score/scoring.py in the Poker44-subnet repo):

Training code

Full training pipeline (data download, feature engineering, model definitions, calibration, cross-validation): see the source repository.

Intended use

Miner inference for Poker44 subnet 126 DetectionSynapse chunks — one bot-risk score in [0, 1] per chunk of poker hands.

Attestation

• Trained only on the public benchmark endpoint above.
• No validator-only or private data used.
• Open source, MIT licensed.