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numerai-model-implementation

Register and integrate a new Numerai model type into the agents training pipeline.

1.2k|310|Updated Jan 6, 2017
Search Tags:#config#model-registration#numerai#model_factory#agent-pipeline#fit-predict#custom-model
One-click install
npx skills add https://github.com/numerai/example-scripts --skill numerai-model-implementation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: numerai-model-implementation
Source: https://github.com/numerai/example-scripts/tree/main/numerai/agents/skills/numerai-model-implementation
Command: npx skills add https://github.com/numerai/example-scripts --skill numerai-model-implementation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables data scientists and software engineers to register and integrate a new Numerai model type into the agents training pipeline, ensuring a clean, reusable interface for future models.

Core Features & Use Cases

  • Model API & Wrappers: Define a consistent fit(X, y, sample_weight=...) and predict(X), and colocate model-specific logic under isolated wrappers.
  • Factory Registration: Update the model_factory to lazily import and construct the new model type, with clear ImportError messages if dependencies are missing.
  • Config & Data Integration: Extend configs and data preparation utilities so the new model can be selected in experiments and run via python -m agents.code.modeling.

Quick Start

From the repo root, add your model wrapper, update the factory, and configure a test run with your new model type. Then execute the smoke test to validate integration.

Frequently Asked Questions about numerai-model-implementation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate a custom Numerai model into the agent training pipeline?▼

To integrate a custom Numerai model, define a consistent fit and predict API, wrap the model logic, register it in the model_factory, and update configuration to run via python -m agents.code.modeling.

What is model_factory registration used for in Numerai pipelines?▼

Model_factory registration maps model types to their respective classes using lazy imports, ensuring clean dependency management and providing clear ImportError messages if required libraries are missing.

Does the Numerai model integration require specific data preparation utilities?▼

Yes, integrating a new model requires extending existing configuration and data preparation utilities so the model can be properly selected in experiments and process the correct input formats.

How do I ensure reproducible experiments when adding a new Numerai model type?▼

Reproducible experiments are achieved by isolating model-specific logic under wrappers, maintaining a strict fit(X, y, sample_weight=...) interface, and extending configuration files for consistent execution.

Why are lazy imports necessary for registering custom models in the agent pipeline?▼

Lazy imports are necessary to prevent environment crashes by delaying dependency loading until the specific model type is called, providing clear ImportError messages if dependencies are missing.

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