toys is a simple simulated dataset of a binary classification
problem, introduced by Weston et.al..
The format is a list of 2 component:
$x: A data-frame containing input variables: with 100 obs. of 200 variables ;
$y: Output variable: a factor with 2 levels "-1" and "1".
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 Xi 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.
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
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