I'm reading a paper on data mining, and the authors mentioned that "we bootstrapped a classifier". I understand that bootstrapping means sampling with replacement, but I didn't understand how that relates to classifiers.
So I figured I'd sum up what I've learned about McFadden's pseudo $R^2$ as a proper answer. 

The seminal reference that I can see for McFadden's pseudo $R^2$ is: McFadden, D. (1974) “Conditional logit analysis of qualitative choice behavior.” Pp. 105-142 in P. Zarembka (ed.), Frontiers in Econometrics. Academic Press. http://eml.berkeley.edu/~mcfadden/travel.html
Figure 5.5 shows the relationship between $\rho^2$ and traditional $R^2$ measures from OLS. My interpretation is that larger values of $\rho^2$ (McFadden's pseudo $R^2$) are better than smaller ones.  

The interpretation of McFadden's pseudo $R^2$ between 0.2-0.4 comes from a book chapter he contributed to: Bahvioural Travel Modelling. Edited by David Hensher and Peter Stopher. 1979. McFadden contributed Ch. 15 "Quantitative Methods for Analyzing Travel Behaviour on Individuals: Some Recent Developments". Discussion of model evaluation (in the context of multinomial logit models) begins on page 306 where he introduces $\rho^2$ (McFadden's pseudo $R^2$). McFadden states "while the $R^2$ index is a more familiar concept to planner who are experienced in OLS, it is not as well behaved as the $\rho^2$ measure, for ML estimation. Those unfamiliar with $\rho^2$ should be forewarned that its values tend to be considerably lower than those of the $R^2$ index...For example, values  of 0.2 to 0.4 for $\rho^2$ represent EXCELLENT fit."

So basically, $\rho^2$ can be interpreted like $R^2$, but don't expect it to be as big. And values from 0.2-0.4 indicate (in McFadden's words) excellent model fit. 
