Search Results - subject:"variable importance"

Forecasting the direction of the Fed's monetary policy decisions using random forest

Yoon, Jungyeon; Fan, Juanjuan - In: Journal of forecasting 43 (2024) 7, pp. 2848-2859

Persistent link: https://www.econbiz.de/10015110569

The anatomy of out-of-sample forecasting accuracy

Borup, Daniel; Goulet Coulombe, Philippe; Rapach, David E. - 2022

We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

Persistent link: https://www.econbiz.de/10014278179

Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning

Jin, Yutong; Benkeser, David - In: Journal of Causal Inference 10 (2022) 1, pp. 280-295

Abstract Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of neutralizing many possible strains of...

Persistent link: https://www.econbiz.de/10014610925

The anatomy of out-of-sample forecasting accuracy

Borup, Daniel; Goulet Coulombe, Philippe; Rapach, David E. - 2022

Persistent link: https://www.econbiz.de/10013429204

Balancing and variable reduction of firm bankruptcy data

Olson, David L.; Chae, Bongsug - In: Journal of supply chain management science : JSCMS 3 (2022) 1/2, pp. 3-15

Financial stress experienced by supply chain elements causes stress to all members. Predictive data mining is a common tool for predicting bankruptcy. Bankruptcy often involves highly imbalanced datasets with a large number of potential variables, with bankrupt firms being by far the minority...

Persistent link: https://www.econbiz.de/10013447669

Improving explainability of major risk factors in artificial neural networks for auto insurance rate regulation

Xie, Shengkun - In: Risks 9 (2021) 7, pp. 1-21

rate regulation purposes. This requirement may imply the need for estimating or evaluating the variable importance when …. ANN models are applied to meet this goal, and variable importance is measured to improve the model's explainability due to … the models' complex nature. The results obtained from different variable importance measurements are compared, and …

Persistent link: https://www.econbiz.de/10013200792