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Bootstrap Aggregating and Random Forest · Study

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ChapterBootstrap Aggregating and Random Forest

Tae‐Hwy Lee2019

Chapter2019

Bootstrap Aggregating and Random Forest

Tae‐Hwy Lee · Aman Ullah · Ran Wang

167 citations·0/0 files stored·Advanced studies in theoretical and applied econometrics

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Forecasting Techniques and Applications Statistical Methods and Inference Machine Learning and Data Classification

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References

2007A Comprehensive Look at The Empirical Performance of Equity Premium Prediction 2008How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation

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2023An integrated approach of ensemble learning methods for stock index prediction using investor sentiments 2020The Macroeconomy as a Random Forest 2024Using machine‐learning methods in meta‐analyses: An empirical application on consumer acceptance of meat alternatives

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Year2019

Typechapter

Published2019-11-28

VenueAdvanced studies in theoretical and applied econometrics

ISSN-L1570-5811

Languageen

Citations167

Files0 public / 0

ShelfForecasting Techniques and Applications

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doi10.1007/978-3-030-31150-6_13

openalex_workW2990979467

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