GitHub / mazba-ahamad / Generating-Composite-Proxy-Target-Variable-for-Machine-Learning-Models-of-Business-Decisions
A multi-criteria decision analysis (MCDA)-based composite proxy target variable generation technique for business decision modeling that uses relevant features or independent variables conceptually related to the intended target variable.
Stars: 1
Forks: 0
Open issues: 0
License: mit
Language: Jupyter Notebook
Size: 34.2 KB
Dependencies parsed at: Pending
Created at: over 2 years ago
Updated at: over 2 years ago
Pushed at: over 2 years ago
Last synced at: about 2 years ago
Topics: business-analytics, business-decisions, composite-indicator, decision-analysis, feature-extraction-algorithm, machine-learning, mcda, ml-pipeline, model-development, multi-criteria-decision-making, predictive-modeling, proxy-model, scoring-algorithm, site-selection, store-selection, topsis