Interpretable AI
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About
Interpretable AI is a comprehensive software suite offering various modules designed for machine learning tasks with a strong emphasis on interpretability. It includes IAIBase for common functionalities like dataset preparation, model training, prediction, evaluation, and grid search. IAITrees helps work with decision tree learners, offering visualization options. OptimalTrees implements algorithms for classification, regression, survival, prescription, and policy learning. OptImpute handles missing data imputation, and OptimalFeatureSelection conducts feature selection. Other modules include RewardEstimation and Heuristics for training models like random forests and XGBoost. The suite, implemented in Julia, provides interfaces for Python and R, making its powerful interpretable AI capabilities accessible to a wider audience. Case studies are also available to showcase its real-world applications.
Platform
Features
• support for heuristic models like xgboost
• grid search for parameter tuning
• model training, prediction, and evaluation
• python and r language interfaces
• optimal feature selection
• missing data imputation (optimpute)
• optimal trees algorithm for various ml problems
• interpretable decision trees
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