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model-experiment

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Train and evaluate challenger models quickly.

Data & Analytics#evaluation#train#model-experiments#CatBoost#ml-retraining#nba-props
Authornajicham
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This skill enables teams to train and evaluate challenger machine learning models for NBA player prop predictions, enabling rapid experimentation against baselines.

Core Features & Use Cases

  • Train regression models on recent data and compare to V9 baseline.
  • Train breakout classifier models to identify breakout games.
  • Support monthly retraining workflows and easy experiment tracking.

Quick Start

Use the model-experiment skill to kick off common experiments:

  • Default regression retrain: PYTHONPATH=. python ml/experiments/quick_retrain.py --name "FEB_MONTHLY"
  • Breakout classifier training: PYTHONPATH=. python ml/experiments/train_breakout_classifier.py --name "BREAKOUT_V1"
  • Dry run: PYTHONPATH=. python ml/experiments/quick_retrain.py --name "TEST" --dry-run

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: model-experiment
Download link: https://github.com/najicham/nba-stats-scraper/archive/main.zip#model-experiment

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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