Reinforcement Learning Trees

By T. Landelius, M. Borga, Hans Knutsson. 1996

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In Reinforcement Learning

Citations in the corpus

All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, methodology): “…are provided at http://jmlr.org/papers/v20/18-760.html. the decrease in prediction accuracy when a covariate is permuted (Breiman, 2001; Breiman et al., 2001; see also Strobl et al., 2008; Altmann et al., 2010; Zhu et al., 2015; Gregorutti et al., 2015; Datta et al., 2016; Gregorutti et al., 2017).”
Consistency of Random Forests (2014, background): “In recent years, various theoretical studies (e.g., Biau et al., 2008; Ishwaran and Kogalur, 2010; Biau, 2012; Genuer, 2012; Zhu et al., 2012) have been performed, analyzing consistency of simpliﬁed models, and moving ever closer to practice.”
All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, unknown): “The Rashomon effect has also been considered in several subject areas outside of VI, including those in non-statistical academic disciplines (Heider, 1988; Roth and Mehta, 2002).”
A random forest guided tour (2015, methodology): “Similarly, the Reinforcement Learning Trees (RLT) of Zhu et al. (2015) build at each node a random forest to determine the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split.”
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests (2014, methodology): “Zhu et al. (2015) prove consistency for their Reinforcement Learning Trees, where embedded random forests are used to decide splitting variables, and achieve signiﬁcant improvements in empirical MSE for some data sets.”
On the asymptotics of random forests (2014, background): “For instance, tree construction can be independent of D n (Biau, 2012), depend only on the X i ’s (Biau et al., 2008) or depend on the whole training set (Cutler and Zhao, 2001; Geurts et al., 2006; Zhu et al., 2012).”
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies (2021, background): “followed by a rich line of research on the topic [4, 34, 56, 57, 131, 156].”
Grouped variable importance with random forests and application to multiple functional data analysis (2014, unknown): “ of Xjwhich is also independent of Y and of all other predictors. This criterion evaluates the increase of the prediction 2 error after breaking the link between the variable Xjand the outcome Y (see Zhu et al. (2012) for instance). In this paper, we extend the permutation importance to groups of va”
Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites (2020, methodology): “Zhu et al. (2015) explained the statistical definition of the variable importance measure using RF.”
Correlation and variable importance in random forests (2013, methodology): “This quantity is the empirical counterpart of the permutation importance measure I (X j ), as formalized recently in Zhu et al. (2012). Let X( j) = (X1, .”