Russ mentions using the XGBoost add-in in this segment. I can't find the add-in on the site. Where is it? Is it a JMP Pro only add-in?
Yes, the add-in is only available with JMP Pro as it runs off the Gradient Boosted Tree that is a JMP Pro feature. If you are interested in trying out the add-in reach out to @Jason_Wiggins he will be able to add you to the list.
@russ_wolfingerYou mentioned you would share the Covid cumulative death rate/ infection rate data table you were working with where you demonstrated some operations on the columns (log and lag by 7 says) and then demonstrated the graphing and eventually the use of XGBoost for prediction?
I'd love to play with the dataset and use JMP's 'default' predictive modeling platforms as a learning exercise for myself (in addition to using the XGBoost add-in, which to which I've reached out to @Jason_Wiggins for the Pro add-in as I understand it is only available for Pro at this time.
Hi @PatrickGiuliano , thanks, the table is an attachment just above the video. It's a couple of weeks old though now. For the latest, check out https://www.kaggle.com/c/covid19-global-forecasting-week-4/overview
I just invited you to the XGBoost Early Adopter group. Once in there, click the Welcome message and follow the provided instructions.
@allThis group is still open to anyone interested, please message me or @mia_stephens if you would like to join.
Thanks @russ_wolfinger!
I see it now in the Kaggle data hyperlink that you included above and directly below the posted video. I re-copy the latest data query link here for my own reference and in service of the community: https://www.kaggle.com/c/covid19-global-forecasting-week-4/overview
