# theory

> This document explains the theoretical foundations of SHAP (SHapley Additive exPlanations), including Shapley values from game theory, the principles that 

- Source: https://doutorai.pt/en/components/skills__theory
- Type: Skills
- Category: Scientific
- Downloads: 0
- Site: Doutor AI (https://doutorai.pt/en) - AI tools for small businesses

An open-source community component, curated and adapted by Doutor AI.

## What it does and how to use it

A **skill** that teaches Claude Code to do scientific tasks well. It is packaged knowledge the model uses whenever it is relevant.

1. Install the skill (command above).
2. In Claude Code, describe what you need - the skill turns on by itself when the context fits.
3. No commands to memorize: you talk in plain language and the skill does the rest.

## Install (Claude Code)

```bash
mkdir -p .claude/skills/theory && curl -fsSL https://doutorai.pt/data/lib/skills__theory.md -o .claude/skills/theory/SKILL.md
```

Paste this command into the terminal, in your project folder, and the component installs automatically.

## Content

```
# SHAP Theoretical Foundation

This document explains the theoretical foundations of SHAP (SHapley Additive exPlanations), including Shapley values from game theory, the principles that make SHAP unique, and connections to other explanation methods.

## Game Theory Origins

### Shapley Values

SHAP is grounded in **Shapley values**, a solution concept from cooperative game theory developed by Lloyd Shapley in 1951.

**Core Concept**:
In cooperative game theory, players collaborate to achieve a total payoff, and the question is: how should this payoff be fairly distributed among players?

**Map

- 🔒 Full content on the Pro plan (€7.99/month) -
```

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Content by Doutor AI (https://doutorai.pt/en). When quoting, please cite the source: https://doutorai.pt/en/components/skills__theory