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Targeting predictors in random forest regression
Borup, Daniel; Christensen, Bent Jesper; Mühlbach, … - 2020 - This version: May 5, 2020
Persistent link: https://www.econbiz.de/10012317696
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A self-learning algorithm for identifying the leverage points in soil data using quantile regression forests
Rose, Sareena; Nickolas, S.; Sunoj, S. M.; Sangeetha, S. - In: Decision analytics journal 10 (2024), pp. 1-13
Quantile Regression Forest that builds a framework out of the training data set. Unlike other supervised anomalous detection …
Persistent link: https://www.econbiz.de/10014541523
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Forecasting domestic tourism across regional destinations through MIDAS regressions
Nava, Consuelo Rubina; Osti, Linda; Zoia, Maria Grazia - 2022
Persistent link: https://www.econbiz.de/10013366573
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Bagging in tourism demand modeling and forecasting
Athanasopoulos, George; Song, Haiyan; Sun, Jonathan A. - In: Journal of travel research : a quarterly publication of … 57 (2018) 1, pp. 52-68
Persistent link: https://www.econbiz.de/10011915335
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Nowcasting mit Internet-Suchmaschinen : fragen wir Google!
Schmidl, Matthias - 2015 - 1. Aufl
Persistent link: https://www.econbiz.de/10011398082
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Targeting predictors in random forest regression
Borup, Daniel - 2020
Random forest regression (RF) is an extremely popular tool for the analysis of high-dimensional data. Nonetheless, its … the forest, is balanced at a medium degree of targeting, selecting the best 10-30% of commonly applied predictors …
Persistent link: https://www.econbiz.de/10012839887
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Macroeconomic forecasting using factor models and machine learning : an application to Japan
Maehashi, Kohei; Shintani, Mototsugu - 2020
Persistent link: https://www.econbiz.de/10013329759
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Regression and decision tree approaches in predicting the effort in resolving incidents
Christa, Sharon; Suma, V.; Mohan, Uma - In: International journal of business information systems : … 39 (2022) 3, pp. 379-399
Persistent link: https://www.econbiz.de/10013198436
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Predicting financial performance for listed companies in Thailand during the transition period : a class-based approach using logistic regression and random forest algorithm
Pornpawee Supsermpol; Huynh, Van-Nam; Suttipong … - In: Journal of open innovation : technology, market, and … 9 (2023) 3, pp. 1-16
the stock market. The study demonstrates that the machine learning method (random forest algorithm) outperforms the …
Persistent link: https://www.econbiz.de/10014504880
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Predicting financial distress in Latin American companies : a comparative analysis of logistic regression and random forest models
Barboza, Flavio Luiz de Moraes; Altman, Edward I. - In: The North American journal of economics and finance : a … 72 (2024), pp. 1-14
Persistent link: https://www.econbiz.de/10014534855