5.2 IRLS Parameter Evaluation and Elimination

Table 5.3: IRLS Parameters

Parameter	Description
modelmin	Lower threshold for $\mu = \exp(\eta) / (1+\exp(\eta))$
modelmax	Upper threshold for $\mu = \exp(\eta) / (1+\exp(\eta))$
wmargin	Symmetric threshold for weights $w = \mu (1-\mu)$
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
lreps	Deviance epsilon for IRLS iterations
cgeps	Residual epsilon for CG iterations
cgdeveps	Deviance epsilon for CG iterations
lrmax	Maximum number of IRLS iterations
cgmax	Maximum number of CG iterations
cgbinit	CG iterations for IRLS iteration $i$ start where IRLS iteration $i-1$ stopped

Figure 5.3: LR tree with rectangle marking the beginning of our IRLS implementation.