by-campaign-optimizer
CommunityData-driven next-round design parameter optimization.
Data & Analytics#thresholds#campaign#optimizer#multi-round#active-learning#random-forest#feature-importances
Author001TMF
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Automates the generation of data-driven next-round campaign parameters for multi-round design campaigns by learning from scored designs.
Core Features & Use Cases
- Active-learning optimization: trains a Random Forest on existing scores to derive thresholds and design counts.
- Exploration guidance: outputs regions in feature space where sampling is most informative.
- Config automation: emits a ready-to-use next-round YAML config and an audit trail in optimizer metadata.
Quick Start
Run the optimizer on your scored designs to produce a next-round YAML config with recommended thresholds and design counts.
Dependency Matrix
Required Modules
numpypandasscikit-learnpyyaml
Components
scripts
💻 Claude Code Installation
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Please help me install this Skill: Name: by-campaign-optimizer Download link: https://github.com/001TMF/blatant-why/archive/main.zip#by-campaign-optimizer Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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