9.9 Meeting Videos

9.9.1 Cohort 1

Meeting chat log
00:14:27    Jonathan Trattner:  Is there supposed to be sound?
00:16:42    Jonathan Trattner:  I love how R’s parent S is up next
00:24:03    Asmae Toumi:    Amazing
00:24:49    Scott Nestler:  This whole chapter reminds me of a classic 2010 paper from my professor and friend Galit Shmueili, "To Explain or to Predict?"  https://projecteuclid.org/journals/statistical-science/volume-25/issue-3/To-Explain-or-to-Predict/10.1214/10-STS330.full
00:25:48    Scott Nestler:  Correction … her last name is Shmueli (had an extra 'i' in there).
00:26:08    Andy Farina:    This chapter reminded me of a quote I have heard numerous times from my advisor over the past few years…”All models are wrong, some are useful”
00:26:24    Jon Harmon (jonthegeek):    Hehe, yup!
00:28:11    Ben Gramza: I seem to hear that George Box quote 10000000 times a year
00:28:42    Conor Tompkins: map() go brrrrr
00:30:25    Conor Tompkins: Would be cool to create a raster of the density of the points, and find the differences between models
00:30:45    Jon Harmon (jonthegeek):    I'm a little skeptical, we'll have to dig into that code!
00:31:30    Scott Nestler:  Something doesn't seem right.  Some of these metrics should *not* come up with the same results.
00:35:49    Conor Tompkins: Can you expect higher RMSE in general for higher priced homes? Ie $3 million. Is it better to use a percentage error term if that is the case?
00:54:24    Jonathan Trattner:  Gotta head out now. Thanks Joe! Great job!
00:58:20    Asmae Toumi:    Cool thanks
00:59:54    Tyler Grant Smith:  like rock is correct pronunciation
01:02:10    Scott Nestler:  A macro-average will compute the metric independently for each class and then take the average (hence treating all classes equally), whereas a micro-average will aggregate the contributions of all classes to compute the average metric.
01:02:21    Scott Nestler:  The weighted macro computes them independently, but weights them by number of observations, rather than equally.  Usually better than regular macro when there are class imbalances.
01:06:47    Andy Farina:    Excellent Joe, thank you!

9.9.2 Cohort 2

Meeting chat log
00:10:24    Amélie Gourdon-Kanhukamwe (she/they):   https://www.meetup.com/tech-ethics-bristol/
00:11:52    Janita Botha:   it's a little small but ok
00:12:34    Janita Botha:   that is perfect!
00:27:39    Janita Botha:   sidewarren - but I really like the idea of a "stupid" model. What would be the equivalent in a classification model?
00:28:45    rahul bahadur:  I think that would be simply assigning all predictions to one value out of the 2
00:55:51    Janita Botha:   bye folks!

9.9.3 Cohort 3

Meeting chat log
00:13:11    Ildiko Czeller: my connection is unstable, i am here but without video for now
00:57:20    jiwan:  https://community.rstudio.com/t/predict-a-class-using-a-threshold-different-than-the-0-5-default-with-tidymodels/56273
00:58:29    Toryn Schafer (she/her):    https://www.rdocumentation.org/packages/probably/versions/0.0.6/topics/make_class_pred
00:59:06    Toryn Schafer (she/her):    https://cran.r-project.org/web/packages/probably/vignettes/where-to-use.html
00:59:42    Ildiko Czeller: https://probably.tidymodels.org/reference/append_class_pred.html
01:01:41    Toryn Schafer (she/her):    https://adv-r.hadley.nz/fp.html

9.9.4 Cohort 4

Meeting chat log
00:08:59    Isabella Velásquez: https://twitter.com/WeAreRLadies