For August, we're delighted to have Jared Lander from New York sharing on two topics--one slightly more theoretical and one slightly more applied. Code samples may be included in R or Python. This event will be hosted at Google's new Developer Space =)
*RSVP open Wed, Aug 8,[masked]:00 PM*.
6.30pm - 7.00pm: Registration / Networking
7:00pm - 7.30pm: Many Ways to Lasso
7.30pm - 8.00pm: Scoring Sales Leads
8.00pm - 8.30pm: Closing and Networking
Many Ways to Lasso:
The elastic net is one of Jared's favourite algorithms, implementing both the lasso and ridge, and a combination of the two. The main way to fit the elastic net is with glmnet, written by Hastie, Tibshirani and Friedman. But there are many other ways, including xgboost, Stan and TensorFlow. We fit the elastic net a few ways and see how they work differently.
Scoring Sales Leads:
Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York and Washington DC R Conferences, and an Adjunct Professor at Columbia Business School. He is the author of R for Everyone, a book about R Programming geared toward Data Scientists and Non-Statisticians alike. His writings on statistics can be found at jaredlander.com and he tweets from @jaredlander.