Interpretable AI
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About
Interpretable AI is a software suite designed to enhance the interpretability of machine learning models. It offers several modules that cater to various aspects of model building, such as data preparation, parameter tuning, and evaluation. The primary components include IAIBase for common functionalities, IAITrees for decision tree learners, OptimalTrees for various types of optimal learners, and OptImpute for missing data imputation. The suite also caters to optimal feature selection and reward estimation, along with heuristic models. Interpretable AI supports multiple programming languages, including Julia, Python, and R. Documentation is extensive, offering guides, case studies, and API references for users to effectively utilize the software. It is suitable for researchers and practitioners looking to improve the transparency and interpretability of their machine learning workflows.
Platform
Features
• data preparation
• model evaluation
• prescriptive problem solving
• feature selection
• missing data imputation
• optimal learners
• decision tree visualization
• parameter tuning
• model training and prediction
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