Random Forests

In the citing paper

“RF uses the principle of majority voting; the most common prediction from all decision trees is the final classification [17]”

In the citing paper

“RF is an ensemble approach that combines the predictive classifications of multiple decision trees.”

In the citing paper

“Random forests (rf) modify Breiman's ( 2001) traditional bagging approach.”

In the citing paper

“To overcome the black-box nature of GBM, (Breiman 2001; Friedman and Popescu 2008; Hastie et al. 2009) introduced two important tools to interpret the GBM model: (i) variable importance measures and (ii) partial dependence plots.”

In the citing paper

“We prioritised Random Forest (RF) [15] as our primary baseline due to its high interpretability and resistance to overfitting in high-dimensional feature spaces.”

In the citing paper

“Combining the predictions of many different models is a very successful way to reduce test errors, 1, 3”

In the citing paper

“In order to improve the generalization performance of decision tree, the random forest [306] is further investigated by integrating decisions from multiply tree-based classifiers.”

In the citing paper

“RF is an ensemble method based on decision trees and uses bagging to reduce overfitting (Breiman, 2001).”

In the citing paper

“RF is a robust algorithm for dealing with multicollinearity and allows for non‐linear relationships between the covariates and the dependent variable (Breiman, 2001).”

In the citing paper

“Feature importance was reported from the fitted random forest as a relative measure of variable use and was not interpreted causally because known-site patterns may also reflect preservation, accessibility, survey history and reporting bias (Breiman 2001).”

In the citing paper

“While RF limits overfitting when correctly parameterized [36,89,95], the near-perfect ensemble AUC score (0.999) suggests that it may be over-specified, with both calibration and validation influenced by the assumption that the absence data are true absences.”

In the citing paper

“RF differs from GLM, GAM, and MaxEnt in that it has no underlying assumptions about distributions when model fitting [89].”

In the citing paper

“It is the most powerful predictive model used in our analysis [89].”

In the citing paper

“Non-parametric ML techniques like Random Forest126 have been successfully used to predict species richness and have shown significant error reduction with respect to the traditional counterparts used in ecology, for example in the estimation of richness distributions of fishes127,128, spiders 129, and small mammals130.”

In the citing paper

“The most celebrated theoretical result is that of Breiman (2001), which offers an upper bound on the generalization error of forests in terms of correlation and strength of the individual trees.”

In the citing paper

“First of all, in Breiman's forests, the criterion (2) is evaluated over a subset M try of randomly selected coordinates, and not over the whole range { 1 , . . . , p } .”

In the citing paper

“Interestingly, picking a large M does not lead to overfitting.”

In the citing paper

“As well as this, in Breiman's (2001) forests, each leaf (that is, a terminal node) of individual trees contains a small number of observations, typically between 1 and 5.”

In the citing paper

“At last, each tree is constructed on a subset of a n examples picked within the initial sample, not on the whole sample D n ; and only these a n observations are used to calculate the estimation.”

In the citing paper

“Scornet et al. (2015), who show that Breiman's (2001) forests are consistent in an additive regression framework.”

In the citing paper

“Besides, the individual trees are not pruned, and the final cells do not contain more than nodesize observations (unless all data points in the cell have the same X i ).”

In the citing paper

“In particular, the choice k ≈ log n corresponds to obtaining a small number of examples in the leaves, in accordance with Breiman's (2001) idea that the individual trees should not be pruned.”

In the citing paper

“A seminal result by Breiman (2001) shows that the error of the forest is small as soon as the predictive power of each tree is good and the correlation between the tree errors is low.”

In the citing paper

“We note however that if Breiman's (2001) original algorithm is used in this example (that is, each cell contains exactly one data point) then one obtains a consistent classification rule.”

In the citing paper

“More precisely, independently of the type of forest, one has”

In the citing paper

“For activity classification we use random forests 50 which offer a powerful nonparametric discriminative method for multi-class classification that offers state-of-the-art performance 51 .”