Domain-Aware ML (Mixture of Experts)

Specialized ML models for different market categories.

Overview

Instead of one generic model, Predicta uses domain-specific specialist models for better accuracy:

Domain	Model	Domain Features
`crypto`	`crypto_model.keras`	BTC correlation, gas, funding rate
`sports`	`sports_model.keras`	Team ELO, injuries, weather
`politics`	`politics_model.keras`	Polling, incumbency, news sentiment
`entertainment`	`entertainment_model.keras`	Social mentions, trending score
`science`	`science_model.keras`	Historical success, regulatory stage
`generic`	`generic_model.keras`	Fallback for rare categories

Architecture

Market → [Domain Router] → Category → [Specialist Model] → P(YES)
              │                             │
         Keywords/Tags              10-dim domain features
         Polymarket tags            + GRU + Attention encoder

Files

File	Purpose
`cortex/domain_router.py`	Classify markets into domains
`cortex/domain_features.py`	Extract domain-specific features
`cortex/training/bootstrap_by_domain.py`	Create domain-separated datasets
`cortex/training/train_domain_model.py`	Train specialist models
`cortex/inference_server.py`	`/predict_domain` endpoint

Usage

1. Generate Datasets

cd cortex/training
python bootstrap_by_domain.py

# Output:
# data/crypto_dataset.pkl
# data/sports_dataset.pkl
# data/politics_dataset.pkl
# data/generic_dataset.pkl

2. Train Models

python train_domain_model.py --domain crypto
python train_domain_model.py --domain sports
python train_domain_model.py --domain all

# Output:
# models/crypto_model.keras
# models/sports_model.keras

3. Inference

Models auto-load on server startup. Use the new endpoint:

POST /predict_domain
{
  "token_id": "...",
  "title": "Will Bitcoin reach $100k?",
  "volume": 1500000,
  "tags": ["crypto"]
}

Response includes domain routing info:

{
  "probability": 0.72,
  "uncertainty": 0.04,
  "domain": "crypto",
  "domain_confidence": 0.85,
  "model_used": "crypto_model"
}

Fallback Behavior

If router confidence < 50% → Use generic_model
If domain model missing → Use generic_model
If generic missing → Use legacy model
If all fail → HTTP 503

Getting Started

System Architecture

Features & Logic

Guides

Operational Status

Archive

Domain aware ml

Domain-Aware ML (Mixture of Experts)

Overview

Architecture

Files

Usage

1. Generate Datasets

2. Train Models

3. Inference

Fallback Behavior

​Domain-Aware ML (Mixture of Experts)

​Overview

​Architecture

​Files

​Usage

​1. Generate Datasets

​2. Train Models

​3. Inference

​Fallback Behavior

Domain-Aware ML (Mixture of Experts)

Overview

Architecture

Files

Usage

1. Generate Datasets

2. Train Models

3. Inference

Fallback Behavior