Mat = lapply(c("LogitBoost", 'xgbTree', 'rf', 'svmRadial'), With the caret package, the training is so easy that I’ve added boosted logistic regression and SVM ,just in case’’. I am going to use data from The Cancer Genome Atlas Project (next generation sequencing, expression of mRNA, 33 different tumors, 17000+ features, 300+ cases, 33 different classes) and the classifier should predict the type of cancer based on gene expression (Actually I am interested in genetic signatures, but classification is the first step).ĭataset is going to be divided into a testing and training data and the whole procedure will be replicated hundreds times to see what is the variability in model performance. Random forest have tag “rf” while gradient boosting “ xgbTree“. I am going to use the caret package (a really really great package) to compare both methods. You will find more details on slides, and if you prefer videos rather than slides with math, you can watch this example. For example Trevor Hastie said thatīoosting > Random Forest > Bagging > Single Tree The literature shows that something is going on. But recently here and there more and more discussions starts to point the eXtreme Gradient Boosting as a new sheriff in town. Also one may use bagging instead of boosting so there are much more choices.įor me, the random forest if one of favorite tools when it comes to genetic data (because of OOB, proximity scores and feature importance scores). The other interesting approach is to use a gradient boosting method, to create a collection of trees that optimize the cases that are badly predicted by previous trees. One of the most popular solutions is to create a random forest, an ensemble of trees that vote independently, each tree is build on bootstrap sample of observations and subset of features. Unfortunately they are quite unstable, particularly for large sets of correlated features.įortunately, there are some solutions that may help. It is easy to visualize them, easy to explain, easy to apply and even easy to construct.
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