Розмір відео: 1280 X 720853 X 480640 X 360
Показувати елементи керування програвачем
Автоматичне відтворення
Автоповтор
6:25 How can this "modern" survival analysis be cooler than actuarial analysis even though it is the same thing?
obviously, it is cooler because he is doing it now...teehee...
do you have the code for the survival regression or even slides.
Is the GitHub repo for this still available somewhere?
Thanks a lot :) simple and precise.
HUGE fan of CDP. What an impact he has made on the data science community and modern business!
im still confused with what duration should be. should it be duration they were in power or duration from start to current_date, since we talk about censorship as well?
Francisco Mendoza if end is unknown. Take the time upto the date of analysis.
Francisco Mendoza also there is a datetime smtg function in lifelines docs. That saves the trouble
I was wondering whether this algo can be used for demand forecasting models with capacity peaks
could you please leave that "dd.csv" somewhere? I can only find different versions, I think. Cheers.
Thank you, Samuel! I'll try again!
import lifelines data = lifelines.datasets.load_dd()
the dataset is in the lifelines library
Hi Ted Mosby
Great talk!
Need to improve typing skill :D
wow this guy is so hot. I wish I had my statistical teacher like him back in uni
6:25 How can this "modern" survival analysis be cooler than actuarial analysis even though it is the same thing?
obviously, it is cooler because he is doing it now...teehee...
do you have the code for the survival regression or even slides.
Is the GitHub repo for this still available somewhere?
Thanks a lot :) simple and precise.
HUGE fan of CDP. What an impact he has made on the data science community and modern business!
im still confused with what duration should be. should it be duration they were in power or duration from start to current_date, since we talk about censorship as well?
Francisco Mendoza if end is unknown. Take the time upto the date of analysis.
Francisco Mendoza also there is a datetime smtg function in lifelines docs. That saves the trouble
I was wondering whether this algo can be used for demand forecasting models with capacity peaks
could you please leave that "dd.csv" somewhere? I can only find different versions, I think. Cheers.
Thank you, Samuel! I'll try again!
import lifelines
data = lifelines.datasets.load_dd()
the dataset is in the lifelines library
Hi Ted Mosby
Great talk!
Need to improve typing skill :D
wow this guy is so hot. I wish I had my statistical teacher like him back in uni