title: The parameter sensitivity of random forests.
abstract: BACKGROUND
  The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to numerous assertions regarding the performance reliability of the default parameters, many RF models are fit using these values. However there has not yet been a thorough examination of the parameter-sensitivity of RFs in computational genomic studies. We address this gap here.
  
  
  RESULTS
  We examined the effects of parameter selection on classification performance using the RF machine learning algorithm on two biological datasets with distinct p/n ratios: sequencing summary statistics (low p/n) and microarray-derived data (high p/n). Here, p, refers to the number of variables and, n, the number of samples. Our findings demonstrate that parameterization is highly correlated with prediction accuracy and variable importance measures (VIMs). Further, we demonstrate that different parameters are critical in tuning different datasets, and that parameter-optimization significantly enhances upon the default parameters.
  
  
  CONCLUSIONS
  Parameter performance demonstrated wide variability on both low and high p/n data. Therefore, there is significant benefit to be gained by model tuning RFs away from their default parameter settings.
authors: Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Canada.
authors: Barbara F F
authors: BF
authors: Huang
authors: Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Canada. Paul.Boutros@oicr.on.ca.|Department of Medical Biophysics, University of Toronto, Toronto, Canada. Paul.Boutros@oicr.on.ca.|Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada. Paul.Boutros@oicr.on.ca.|MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada. Paul.Boutros@oicr.on.ca.
authors: Paul C
authors: PC
authors: Boutros
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D008962:Models, Theoretical; D015203:Reproducibility of Results
keywords: Computational biology; Ensemble methods; Machine-learning; Microarray; Optimization; Parameterization; Random forest; SeqControl