Monday, November 15, 2004
David,
For the Bayesian Logistic Regression Software, how to choose RIDGE or LASSO? If I use Gaussian prior, does it mean that I use RIDGE algorithm? If Laplace, then LASSO?
This is correct. As to which one is best, that is dataset-specific. However, Laplace/LASSO gives sparse solutions (i.e., many of the parameter estimates are zero) and that can be a decisive advantage sometimes.
For the BMR, I cannot figure out how to run it with correct parameters. Could you give me a simple example? Thank you.
bbrtrain -p 1 mydata.dat mymodel.mod
For the Bayesian Logistic Regression Software, how to choose RIDGE or LASSO? If I use Gaussian prior, does it mean that I use RIDGE algorithm? If Laplace, then LASSO?
This is correct. As to which one is best, that is dataset-specific. However, Laplace/LASSO gives sparse solutions (i.e., many of the parameter estimates are zero) and that can be a decisive advantage sometimes.
For the BMR, I cannot figure out how to run it with correct parameters. Could you give me a simple example? Thank you.
bbrtrain -p 1 mydata.dat mymodel.mod
will train a model with the laplace prior and the default hyperparameter.
If you want to select the hyperparameter by CV do, for example,
bbrtrain -p 1 -C 10 -S 0.01,0.1,1,10,100 mydata.dat mymodel.mod
The -C 10 does 10-fold CV. The -S tries those values for the hyperparameter.
D.
Xiaoxu
