Interpretable AI
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About
Interpretable AI offers a robust suite of software modules designed for creating and understanding machine learning models. It provides common functionality for dataset preparation, model training, prediction, evaluation, and hyperparameter tuning. Key modules include IAIBase for core functionalities, IAITrees for working with decision tree learners and visualization, OptimalTrees for classification, regression, survival, prescription, and policy learning. It also features OptImpute for optimal missing data imputation, OptimalFeatureSelection for classification and regression, RewardEstimation for prescriptive problems, and Heuristics for training models like random forests and XGBoost. The software is implemented in Julia, with interfaces available for Python and R.
Platform
Features
• grid search for parameter tuning
• model training, prediction, and evaluation
• python and r language interfaces
• optimal feature selection
• decision tree visualization
• optimal imputation for missing data
• optimal trees algorithm implementation
• dataset preparation
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