The identification of risk-based groups through tree-based models will inform clinical practice guidelines about having risk-based follow-up care. Relevance (S. Halabi) This study presents a SDT model ...

Other tree ensemble regression techniques include random forest regression (and a variant called bagging regression), and gradient boosting regression. Compared to other regression techniques, ...

Machine learning systems use tree-based models both for classification and regression problems. Mathias said that HHS has also started using new AI technology to speed up counterfeit drug detection at ...

Researchers have developed advanced, non-destructive models to measure the aboveground biomass and volume of Populus ...

Random forest regression is an integrated learning method that combines multiple decision tree models into a more powerful model that can effectively avoid overfitting problems and can handle ...

It is unrealistic to suppose that standard item response theory (IRT) models will be appropriate for all the new and currently considered computer-based tests. In addition to developing new models, we ...

Linear regression models were developed for four ecologically and economically important tree species of Meghalaya, India, viz. Betula alnoides, Duabanga grandiflora, Magnolia champaca and Toona ...

Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been ...