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entropy-weight-method

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Objectively weight indicators with entropy.

Data & Analytics#pandas#normalization#data-analytics#weights#numpy#entropy#multi-criteria
AuthorSPIRAL-EDWIN
Version1.0.0
Installs0

System Documentation

What problem does it solve?

The Entropy Weight Method provides an objective, data-driven way to assign weights to multiple indicators, reducing subjective bias in multi-criteria decision problems.

Core Features & Use Cases

  • Data-driven weighting: computes weights from observed data dispersion using information entropy.
  • Handles mixed indicators: supports positive (benefit) and negative (cost) indicators.
  • Versatile integration: commonly used to generate weights for subsequent ranking methods like TOPSIS and other multi-criteria analyses.
  • Easy to adopt: works with common Python data structures (pandas DataFrames).

Quick Start

Prepare a numeric DataFrame with samples as rows and indicators as columns. Call entropy_weight_method(df, negative_indicators=[...]) to obtain a weights vector and a normalized matrix. Example usage is included in the script.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: entropy-weight-method
Download link: https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000/archive/main.zip#entropy-weight-method

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