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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
Web
Task
model interpreting

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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