| toys {VSURF} | R Documentation |
A simulated dataset called toys data
Description
toys is a simple simulated dataset of a binary classification
problem, introduced by Weston et.al..
Format
The format is a list of 2 components:
- x
a dataframe containing input variables: with 100 obs. of 200 variables
- y
output variable: a factor with 2 levels "-1" and "1"
Details
It is an equiprobable two class problem, Y belongs to {-1,1}, with six
true variables, the others being some noise.
The simulation model is defined through the conditional distribution
of the X_i for Y=y:
with probability 0.7, X^j ~ N(yj,1) for j=1,2,3 and X^j ~ N(0,1) for j=4,5,6 ;
with probability 0.3, X^j ~ N(0,1) for j=1,2,3 and X^j ~ N(y(j-3),1) for j=4,5,6 ;
the other variables are noise, X^j ~ N(0,1) for j=7,...,p.
After simulation, the obtained variables are finally standardized.
Source
Weston, J., Elisseff, A., Schoelkopf, B., Tipping, M. (2003), Use of the zero norm with linear models and Kernel methods, J. Machine Learn. Res. 3, 1439-1461
Examples
data(toys)
toys.rf <- randomForest::randomForest(toys$x, toys$y)
toys.rf
## Not run:
# VSURF applied for toys data:
# (a few minutes to execute)
data(toys)
toys.vsurf <- VSURF(toys$x, toys$y)
toys.vsurf
## End(Not run)