When you consider the lower proportion of unanswered questions on AI overall, there doesn't seem to be much evidence that RL questions have a relatively higher chance of being answered there rather than here compared to other questions. I obtained counts using the search terms: -[reinforcement-learning] is:question closed:no isanswered:yes
, etc., and conducted the following analysis:
# input search data
x <- data.frame( site=c("CV","CV","AI","AI"),
RL.tag=c(TRUE,FALSE,TRUE,FALSE),
answered=c(341,71298,251,1831),
unanswered=c(242, 54360, 83, 761) )
# fit saturated logistic regression model
glm(cbind(answered, unanswered)~site*RL.tag, data=x, family="binomial") -> sat.mod
x # print search data
# site RL.tag answered unanswered
# 1 CV TRUE 341 242
# 2 CV FALSE 71298 54360
# 3 AI TRUE 251 83
# 4 AI FALSE 1831 761
summary(sat.mod) # print model summary
# Coefficients:
# Estimate Std. Error z value Pr(>|z|)
# (Intercept) 0.87798 0.04313 20.357 <2e-16 ***
# siteCV -0.60674 0.04350 -13.947 <2e-16 ***
# RL.tagTRUE 0.22863 0.13376 1.709 0.0874 .
# siteCV:RL.tagTRUE -0.15692 0.15808 -0.993 0.3209
confint(sat.mod,level = 0.95) # print confidence intervals around parameter estimates
# 2.5 % 97.5 %
# (Intercept) 0.79392982 0.9630236
# siteCV -0.69250867 -0.5219484
# RL.tagTRUE -0.02926792 0.4957067
# siteCV:RL.tagTRUE -0.47007321 0.1500825
But I'd caution against reading too much into this kind of analysis—that doesn't control for the different types or varying quality of questions that get asked on the two sites. And tagging behaviour needs to be thought about; e.g., we've got 65 questions with multiarmed-bandit but without reinforcement-learning. And who's to say that comparisons made now will be valid in a year's time, especially if there are only a few users answering RL questions on each site?