Machine Learning Benchmarks and Random Forest Regression

By Mark R. Segal. 2004

788 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d91ea30f4f9de817b29bb4ece00f43cb971822b4

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Citations in the corpus

A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (2013, unknown): “Each random selection (with replacement) of training data is used to build a separate regression tree and the overall performance of the final model depends on the prediction accuracy of each individual tree and the correlation between the residuals of individual trees (Segal 2004).”
An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests (2009, unknown): “The particular data sets investigated by Breiman (2001b) seem to enhance the impression that random forests would not overfit, but this notion was heavily criticized by Segal (2004).”
A comparative analysis of kNN and decision tree methods for the Irish National Forest Inventory (2009, methodology): “Segal (2004) noted that the default value of 5 for regression produced good results, but also indicated that larger values can be used when larger datasets are being classified.”
On Oblique Random Forests (2011, methodology): “Here we use the normalized number of votes, or probability p ∈ [0, 1], which is relatively robust against over-fitting [5, 35, 32].”
Selecting critical features for data classification based on machine learning methods (2020, methodology): “Feature selection methods are often used to increase the generalization potential of a classifier [8, 9].”
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification (2008, background): “Finally, it is worthwhile to mention the work by Segal [18] who questioned Breiman's empirical demonstration of the claim that random forests do not overfit as the number of trees grows [2].”
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, background): “reported a better prediction accuracy may be achieved by regulation of the tree size via limiting the number of splits or the size of nodes for which splitting is allowed [99].”
An Introduction to Machine Learning (2020, background): “On the other hand, random forests are usually less prone to overfitting(45) and require less parameter tuning.(46) This makes random decision forests attractive for smaller datasets or as a baseline method for benchmarking.”
Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, unknown): “A study showed that there exist some data distributions where maximal unpruned trees used in the RF do not achieve as good performance as the trees with smaller number of splits and/or smaller node size [23].”