Submit a Model for Credit Default Classifier

Predicts loan default probability based on borrower data

Finance
Benchmark Requirements

Performance Target

Metric:Auc Roc
Target Value:0.91(higher is better)

Area under the ROC curve

Token Reward

1,000 CRDEF per verified DeltaOne

Tokens minted per unit improvement in Auc Roc

How to Submit Your Model
1

Create Model Entry

Register your model in the Hokusai marketplace. We've pre-filled the benchmark requirements from this proposal. You'll configure your model details, token economics, and performance tracking.

2

Register Your Model

After creating your model entry, register your trained model with the Hokusai registry using the SDK.

Install SDK

pip install git+https://github.com/Hokusai-protocol/hokusai-data-pipeline.git#subdirectory=hokusai-ml-platform

Configure API Key

export HOKUSAI_API_KEY="your-hokusai-api-key-here"

Register Model

Register Credit Default Classifier with token CRDEF

import os
import mlflow
from hokusai.core import ModelRegistry

# Set up MLflow tracking URI
mlflow.set_tracking_uri("https://registry.hokus.ai/api/mlflow")

# IMPORTANT: Use your Hokusai API key, NOT an MLflow token
# The Hokusai API key authenticates both the registry and MLflow
os.environ["MLFLOW_TRACKING_TOKEN"] = os.getenv("HOKUSAI_API_KEY")

# Initialize registry
registry = ModelRegistry()

# Register your model
with mlflow.start_run() as run:
    # Log your model (replace with your actual model)
    mlflow.sklearn.log_model(
        your_trained_model,
        "model",
        registered_model_name="Credit Default Classifier"
    )

    # Register with Hokusai
    model_uri = f"runs:/{run.info.run_id}/model"
    registered_model = registry.register_tokenized_model(
        model_uri=model_uri,
        model_name="Credit Default Classifier",
        token_id="CRDEF",
        metric_name="accuracy",
        baseline_value=0.92,
        additional_tags={"author": "your-name", "version": "1.0"}
    )

    print(f"✅ Model registered successfully: {registered_model.name}")
3

Trigger Evaluation

Once your model is registered, trigger an evaluation run against the benchmark dataset. The system will automatically measure your model's performance against the target criteria.

Evaluation process:

  • Your model runs on the specified evaluation dataset
  • Performance is measured using Auc Roc
  • Results are verified and recorded on-chain
  • Token rewards are calculated based on performance improvement
4

Claim Your Rewards

If your model meets or exceeds the benchmark target, you'll earn token rewards. Tokens are automatically minted and can be claimed from your dashboard.

What to Expect

Evaluation Timeline

Evaluation runs typically complete within 5-30 minutes, depending on model complexity and dataset size. You'll receive notifications when evaluation completes.

Performance Verification

All evaluation results are verified and recorded on-chain to ensure transparency and prevent manipulation. Your model's performance will be publicly visible.

Token Distribution

Tokens are minted automatically when your model achieves performance improvements. The amount is calculated based on the delta between your model's performance and the baseline, multiplied by the tokens-per-delta-one rate.

Support & Documentation

Need help? Visit our model submission guide for detailed instructions, code examples, and troubleshooting tips.