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kaggle-hill-climbing

Combine machine learning models using greedy forward selection.

1|Updated Jun 6, 2026
Search Tags:#machine learning#model optimization#Kaggle#hill climbing#ensemble learning
One-click install
npx skills add https://github.com/OlixIgnacious/agent-skills --skill kaggle-hill-climbing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kaggle-hill-climbing
Source: https://github.com/OlixIgnacious/agent-skills/tree/main/.claude/skills/kaggle-hill-climbing
Command: npx skills add https://github.com/OlixIgnacious/agent-skills --skill kaggle-hill-climbing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, cupy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of building an ensemble of machine learning models to improve prediction accuracy for Kaggle competitions, streamlining the complex process of selecting and optimizing model combinations.

Core Features & Use Cases

  • Hill Climbing Algorithm: Selects models one by one, improving ensemble accuracy.
  • GPU Acceleration: Uses CuPy for fast computation of the metric function.
  • Weight Optimization: Refines model weights after the greedy selection step.
  • Use Case: When you have multiple models' OOF predictions and need to build a winning ensemble without extensive additional training.

Quick Start

Execute the command '/kaggle-hill-climbing' within Claude Code to activate this skill.

Frequently Asked Questions about kaggle-hill-climbing

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

FAQPage Schema
How do I build a machine learning ensemble from existing OOF predictions?▼

Greedy forward selection builds a machine learning ensemble by iteratively choosing the model that maximizes the evaluation metric, improving prediction accuracy without requiring additional model training.

Can I use GPU acceleration for hill climbing ensemble optimization?▼

Yes, GPU acceleration is supported through CuPy, enabling fast computation of the metric function during greedy forward selection and weight optimization for large sets of OOF predictions.

Do I need to install Python packages to run greedy forward selection for Kaggle competitions?▼

Yes, you must install numpy, pandas, scipy, and cupy to execute the greedy forward selection script. Familiarity with Python script execution and machine learning model evaluation is also required.

What is the best way to optimize model weights in a Kaggle ensemble?▼

The best way to optimize model weights is using a two-step approach: greedy hill climbing for initial model selection, followed by dedicated weight optimization to refine the ensemble's prediction performance.

When should I not use hill climbing for ensemble learning?▼

Hill climbing for ensemble learning is not suitable when you lack pre-existing out-of-fold (OOF) predictions from diverse models. The algorithm automates selection and weight optimization but does not train base models from raw data.

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