Analysis of a random forest model. Journal of Machine Learning Research, 13, 1063-1095.G. Biau, "Analysis of a random forests model," Journal of Machine Learning Research, vol. 13, no. 1, pp. 1063-1095, January 2012.Biau, G. (2012). Analysis of a random forests model. Journal of ...
In this paper, we offer an in-depth analysis of a random forests model suggested by Breiman (2004), which is very close to the original algorithm. We show in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the...
Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Gene expression signatures that were ... A Villanueva,Y Hoshida,C Battiston,... - 《Gastroenterology》 被引量: 1008发表: 2011年 A comparison of random forests, boosting and suppo...
We also performed simulation studies that showed random forests outperforms several other machine learning algorithms and has comparable results with a newly developed component-wise Cox boosting model. Thus, pathway-based survival analysis using machine learning tools represents a promising approach in ...
Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis In modeling multivariate failure time data, a class of survival model with random effects is applicable. It incorporates the random effect terms in the lin... ...
forest plot of relative risk using a random effects model (Mantel–Haenszel); and Odd’s ratio was calculated in the absence of significant heterogeneity... S Chatterjee,A Ghose,A Sharma,... - 《Journal of Thrombosis & Thrombolysis》 被引量: 39发表: 2013年 Functional lesional neurosurgery for...
To reduce efforts of data acquisition or to enhance comprehension, a feature selection method is proposed that combines the ranking of the relative importance of each parameter in random forests classifiers with an item categorization provided by computed ABC analysis. Data: The input data space, ...
(according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant ...
The random forest algorithm is a combinatorial model consisting of decision trees\(h_{i} (x_{t} )\). The regression tree takes the mean value based on each terminal node as the overall prediction result. Thus, for the sample\(x_{t} \in R^{j}\), j is the number of features and...
Random forests are used extensively in machine learning either for regression or classification problems because they are robust to the non-linearity of data, they do not require data to be normalized, and they mitigate overfitting without extensive parameter tuning. Random forest models operate by ...