Choosing the size of the calibration set in conformal prediction with ahead::conformalize()
[This article was first published on T. Moudiki's Webpage - R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
In version 0.39.0 of the ahead package, the conformalize() function has been updated to include a new parameter called split_prob. This parameter allows users to specify the proportion of the data that should be used for calibration when constructing conformal prediction intervals.
0 – Install packages
install.packages("pak")
pak::pak("Techtonique/ahead")
library(ahead)
1 – USAccDeaths
par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
h = 20L, split_prob = 0.7)
plot(obj)
2 - AirPassengers
par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
h = 20L, split_prob = 0.7)
plot(obj)
3 - fdeaths
par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
h = 20L, split_prob = 0.7)
plot(obj)
4 - UKDriverDeaths
par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
h = 20L, split_prob = 0.7)
plot(obj)
5 - WWWusage
par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
h = 20L, split_prob = 0.5)
plot(obj)
To leave a comment for the author, please follow the link and comment on their blog: T. Moudiki's Webpage - R.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.