Interpretable AI
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About
Interpretable AI (IAI) offers a suite of software modules designed for interpretable machine learning. Key packages include IAIBase (common functionalities), IAITrees (decision tree tools), OptimalTrees (optimal tree algorithms), OptImpute (missing data imputation), OptimalFeatureSelection (feature selection), RewardEstimation (reward estimation), and Heuristics (heuristic models). The software provides interfaces for Julia, Python, and R, enabling users to implement interpretable AI solutions in various programming environments. Case studies are available showcasing real-world applications.
Platform
Task
Features
• heuristics: training heuristic models like random forests and xgboost
• rewardestimation: reward estimation for prescriptive problems
• optimalfeatureselection: optimal feature selection for classification and regression
• optimpute: optimal imputation algorithm for missing data imputation
• optimaltrees: optimal trees algorithm for classification, regression, survival, prescription, and policy learning problems
• iaitrees: decision tree structure and querying, tree-specific learner functionality, decision tree visualization
• iaibase: dataset preparation, model training, prediction, and evaluation, grid search for parameter tuning, different scoring criteria for model evaluation
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