marginTree.getnonzero {marginTree} | R Documentation |
A function to get important features at each split of the margin tree
marginTree.getnonzero(train.obj, threshold)
train.obj |
Output from call to marginTree |
threshold |
Threshold for feature selection: between 0 and 1 |
marginTree.getnonzero
Does hard thresholding of the
weight vector at each split in the margin tree, to select features
A list– one element per split in the tree– with components
feature.scores |
The coefficient for the selected feature |
left.classes |
The outcome classes assigned to the left branch |
right.classes |
The outcome classes assigned to the right branch |
Rob Tibshirani and Trevor Hastie
Rob Tibshirani and Trevor Hastie. Tech report. Feb. 2006. Margin trees for high-dimensional classification Available at http://www-stat.stanford.edu/~tibs/research.html
#generate some data with 5 classes and 100 features set.seed(543) x=matrix(rnorm(40*1000),nrow=40) y=sort(rep(1:5,8)) x[y==2 | y==3, 1:50]=x[y==2|y==3, 1:50]+1 x[y==3,51:100]=x[y==3,51:100]+1 x[y==4|y==5,1:50]=x[y==4|y==5,1:50]-1 x[y==5, 51:100]=x[y==5,51:100]+1 #train the classifier train.obj<- marginTree(x,y) # examine the selected features at each split junk<- marginTree.getnonzero(train.obj,threshold=.5) summary(junk)