measurement-implementation
CommunityProduction-grade metrics for ML experiments.
Data & Analytics#metrics#statistics#reproducibility#machine-learning#hypothesis-testing#experimental-design
AuthorEmaRimoldi
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a production-ready framework to implement core measurements, analytical references, and rigorous testing for ML experiments, ensuring reproducible metrics and fair comparisons.
Core Features & Use Cases
- Empirical metric implementations for classification, similarity, scaling, and information-theoretic measures.
- Analytical reference modules (OLS, ridge, gradient descent, kernel regression, Bayes-optimal, NTK) to benchmark model behavior.
- Comprehensive comparisons and statistical testing (bootstrap, permutation, multiple corrections) with robust numerical stability guidelines.
Quick Start
Load your experiment plan and hypotheses, then generate and validate metrics using the measurement-implementation workflow.
Dependency Matrix
Required Modules
numpytorchscipy
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: measurement-implementation Download link: https://github.com/EmaRimoldi/Claude-scholar-extended/archive/main.zip#measurement-implementation Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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