Genetics

Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning

6 monthes ago   |   By Oxford Journals

AbstractMotivationSelecting the optimal machine learning model for a given dataset is often challenging. Automated ML has emerged as a powerful tool for enabling the automatic selection of ML methods and parameter settings for the prediction of biomedical endpoints. Here, we apply the tree-based pipeline optimization tool to predict angiographic diagnoses of coronary artery disease. With TPOT, ML models are represented as expression trees and optimal pipelines discovered using a stochastic search method called genetic programming. We provide some guidelines for TPOT-based ML pipeline...
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