Michael Clark

Statistical Philosopher, Brute Empiricist

[Michael Clark @statsdatasci]

📖 Excited to announce my book, Models Demystified, is out Friday! It covers concepts from stats to deep learning, & other topics like uncertainty, causal inference & more!

https://t.co/p2sSd26smc (@CRCPress)
https://t.co/rK1yopnkxi (web)

#DataScience #MachineLearning #AI

[OneSix @Onesixsolutions]

From classic techniques to cutting-edge machine learning, data science models help uncover patterns and power smarter predictions. Check out our latest blog post for key insights from Michael Clark's book "Models Demystified": https://t.co/9f65wP4Bcd

[Michael Clark @statsdatasci]

@IsabellaGhement @CRCPress great, thanks @IsabellaGhement !

[Michael Clark @statsdatasci]

Hey folks! Been a while since I posted about it, but our book on practical data science modeling has come a long way since then. Would love to hear some thoughts on GitHub or here. Hopefully we'll get it done soon and out on @CRCPress in the near future!
https://t.co/DnE31gVB6T

[Michael Clark @statsdatasci]

@IsabellaGhement @CRCPress Hi @IsabellaGhement ! A quick note to say thanks again for your thoughts. Like last time I made Github issue with these suggestions so that we'll be sure to keep in mind. I've already incorporated a lot of it too! Thanks!

[Michael Clark @statsdatasci]

@IsabellaGhement @CRCPress Thanks for your thoughts @IsabellaGhement . You are right the language is too loose/vague in several spots here (and our footnote is not enough). I've already gone ahead and made several of these and related changes. I'll also revisit a couple other spots also. Thanks again!

[Michael Clark @statsdatasci]

Link to the repo:
https://t.co/ybzYqrQwR5

[Michael Clark @statsdatasci]

@JKubale @stephenjwild @StaffanBetner @camjpatrick I posted on fractional and several related options with comparisons here: https://t.co/hNCxWYnT3V
The fractional logit result can be done as a binomial glm with robust SE (among other ways).

I like beta reg if applicable, but that's just me.

[Michael Clark @statsdatasci]

@IsabellaGhement We actually had a whole section devoted to odds vs. log odds vs. probability interpretation that was taken out to shorten things up, but maybe that was hasty!

[Michael Clark @statsdatasci]

@IsabellaGhement @IsabellaGhement Thanks much for your feedback, very helpful! We struggled with how best to present notation quite a bit, so will revisit that. Also the language gets a little too loose at times. @ChelseaParlett I'll make a note of the symmetry too, thanks!

[Michael Clark @statsdatasci]

@atomasevic Borrow away, and yes those will be stacked.

[Michael Clark @statsdatasci]

I've been putting together a book on modeling that I hope will appeal to a wide range of audiences, with examples in Python/R.

You can check out the in-progress work at: https://t.co/DnE31gV3hl. Hope you find it useful, and feedback is appreciated as we continue to work on it!

[Michael Clark @statsdatasci]

@arthurturrell Looks great! Will definitely add to next update!

[Michael Clark @statsdatasci]

@RandVegan I think probably because that's what we use more often, and that when we first started out we actually were going to do things mostly 'by hand' as in my doc https://t.co/6vI2gmBhPz. We also were interested in keeping things as similar as possible between the R and Python code.

[Michael Clark @statsdatasci]

@crcgrubbsd Yes, and very much looking forward to it!

[David Grubbs @crcgrubbsd]

I hope @statsdatasci doesn't mind me announcing that this book will be published by Chapman and Hall/CRC likely in 2024.

[Michael Clark @statsdatasci]

@IsabellaGhement @crcgrubbsd @IsabellaGhement here is fine but as a GitHub issue or even just email works too!

[Michael Clark @statsdatasci]

There are good tools in #rstats (e.g. Robyn) and #python (lightweightmmm), but as noted in the article, you often will just have to roll your own (e.g. via @mcmc_stan or #numpyro).

[Michael Clark @statsdatasci]

Been a while, but here's my new post on marketing/media mix models at the @stronganalytics blog. Those used to time series and mixed modeling might find this an interesting application.

https://t.co/NiSPShbAl2

[Strong Analytics, a OneSix Company @stronganalytics]

Here are some helpful #guidelines for working with tabular #data https://t.co/0ABAJkVBrz #deeplearning #tabulardata