5.3 CG-MLE Parameter Evaluation and Elimination

Table 5.24: CG-MLE Parameters

Parameter	Description
modelmin	Lower threshold for $\mu = \exp(\eta) / (1+\exp(\eta))$
modelmax	Upper threshold for $\mu = \exp(\eta) / (1+\exp(\eta))$
margin	Symmetric threshold for outcomes $y$
binitmean	Initialize $\beta_0$ to E( $\mathbf{y}$)
rrlambda	Ridge-regression parameter $\lambda$
cgwindow	Number of non-improving iterations allowed
cgdecay	Factor by which deviance may decay during iterations
cgeps	Residual epsilon for CG iterations
cgmax	Maximum number of CG iterations
dufunc	Nonlinear CG direction update selection

Figure 5.12: LR tree with rectangle marking the beginning of our CG-MLE implementation.