Random forest Model

Reference sources

Reference material used for orientation; read primary and academic sources first when claims conflict.

1. [S1] Reference

   Breiman (2001) -- introduced random forests, proved consistency for additive models, and proposed the OOB error and permutation importance diagnostics

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2. [S2] Reference

   Breiman (1996) -- bagging predictors, the variance-reduction foundation underlying random forests

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3. [S3] Reference

   Ho (1998) -- random subspace method, the feature-subsampling precursor to Breiman's mtry parameter

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4. [S4] Reference

   Goulet Coulombe, Leroux, Stevanovic, Surprenant (2022) -- large-scale macro forecasting horse race showing random forests competitive with deep learning and penalized regressions

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5. [S5] Reference

   Gu, Kelly, Xiu (2020) -- empirical asset pricing comparison finding random forests competitive with neural networks for stock return prediction

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6. [S6] Reference

   Athey, Tibshirani, Wager (2019) -- generalized random forests for heterogeneous treatment effect estimation, extending the framework to causal inference

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7. [S7] Reference

   Meinshausen (2006) -- quantile regression forests for conditional distribution estimation

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