Lmst

Feeling lucky to be in a job (for now) that I love so much. Just arrived back from a 5 day workshop in Goettingen, Germany. We invited palaeoecologists, palynologists, historians, ethnographers and archaeologists working on the Atlantic Forest of #brazil. We were also honoured to host a Tupi-guarani village leader, who generously offered his unique perspectives. I delivered a 2 day workshop on #gis #rstats #ecology species distribution modelling using #tidymodels and #tidysdm.

A nerdy-looking man points a stick at projector screen showing a slide describing species distribution modelling.

A mostly drunk pint of delicious weisbeer.

Inside the replica lichenstein cave. A burial site for around 65 bronze age individuals in central Germany, who were all found to be part of the same extended family. Many gold and bronze objects were also recovered along with charred plant remains from around ~?60 different species.

New blog post: Spatial Machine Learning with tidymodels 🌍🧠📦

This post shows how to apply the tidymodels framework to spatial data workflows in R. Part 3 in a series about #sml.

🔗 https://geocompx.org/post/2025/sml-bp3/

#rstats #rspatial #tidymodels #gischat

If you are looking to use the cubist algo for regression and want to get it into shape then use the healthyR.ai package and the hai_cubist_data_prepper() function.

Link: https://www.spsanderson.com/healthyR.ai/reference/hai_cubist_data_prepper.html

#regression #tidymodels #data #algorithm #cubist

If you are looking to use the cubist algo for regression and want to get it into shape then use the healthyR.ai package and the hai_cubist_data_prepper() function.

Link: https://www.spsanderson.com/healthyR.ai/reference/hai_cubist_data_prepper.html

#regression #tidymodels #data #algorithm #cubist

I wrote up a little blog post comparing the runtime and memory allocation of how we used to create dummy variables with the new sparse support I added in tidymodels

https://emilhvitfeldt.com/post/sparse-vs-dense-dummies/
#rstats #tidymodels

Happy to share that {recipes} has a new release with many new features and all known bugs exterminated!

https://www.tidyverse.org/blog/2025/04/recipes-1-3-0/
#rstats #tidymodels

If you are looking for data processors to get your data in line for the algo in question, then my #R #package { healthyR.ai } has you covered. These are based on using #tidymodels #parsnip from the #tidyverse

https://www.spsanderson.com/healthyR.ai/reference/index.html#data-preprocessors

#RStats #Data #ModelData

If you are looking for data processors to get your data in line for the algo in question, then my #R #package { healthyR.ai } has you covered. These are based on using #tidymodels #parsnip from the #tidyverse

https://www.spsanderson.com/healthyR.ai/reference/index.html#data-preprocessors

#RStats #Data #ModelData

Hello #rstats #tidymodels #yolo #machinelearning community.

I think I am about to start some image recognition with R, in order to find traffic-calming devices on fine satellite imagery. @cquest has been doing some of that with Python.
Has somebody been there and has some words of advice or tips ?
Thanks !

We heard from the community that CatBoost is the way do go. We listened and learned! here is the first batch of updated hexes for #tidymodels

Grid of 10 hex stickers, all featuring cartoon styled cats. Each sticker represents a tidymodels package.

tune: cat singing with music notes around it
dials: cat being DJ
rsample: cat sitting in a shoe
broom: cat holding a broom
recipes: cat holding whisk and rolling pin
parsnip: cat holding giant parsnip
yardstick: cat holding a ruler
stacks: 3 cats standing on to of each other
poissonreg: cat with heart eyes drowning in fish
workflows: Big cat head overseeing conveyor belt

One of the exciting parts of the new sparse data tidymodels work, is that {textrecipes} can now be used as a reproducible way to generate DTM, tf-idf etc etc
#rstats #tidymodels

library(tidymodels)
library(textrecipes)

data("tate_text", package = "modeldata")

tate_split <- initial_split(tate_text)
tate_train <- training(tate_split)
tate_test <- training(tate_split)

rec <- recipe(~ medium, data = tate_train) |>
step_tokenize(medium) |>
step_tfidf(medium, sparse = "yes") |>
prep()

bake(rec, tate_train, composition = "dgCMatrix") |> head()
#> 6 x 822 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 822 column names 'tfidf_medium_1', 'tfidf_medium_10' ... ]]
#>
#> [1,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
#> [2,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
#> [3,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

bake(rec, tate_test, composition = "dgCMatrix") |> head()
#> 6 x 822 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 822 column names 'tfidf_medium_1', 'tfidf_medium_10' ... ]]
#>
#> [1,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
#> [2,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
#> [3,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

This summer, join the tidymodels team as an intern and help expand the possibilities of feature selection!

Over the years, our eight summer interns have added incredible contributions, including packages like agua, applicable, bundle, butcher, shinymodels, spatialsample, and stacks. Now, it’s your turn to shape the future of #tidymodels #RStats tools! 📊

Learn more and apply: https://www.tidyverse.org/blog/2025/01/tidymodels-2025-internship/

Combining two of my favorite things.
#RStats
and oysters. My latest blog post is a project to predict New York Harbor water quality using data from Billionoysterproject.org and #tidymodels
https://outsiderdata.netlify.app/posts/2024-12-05-predicting-water-quality-in-new-york-harbor/oyster

🚀 Introducing **tidyAML**: the new R package for automated machine learning!

Quickly generate multiple regression models with just a few lines of code, all while leveraging the powerful **tidymodels** ecosystem.

No Java setup needed! Perfect for beginners & pros alike.

Check it out! #rstats #AutoML #DataScience #tidymodels #parsnip

🚀 Introducing **tidyAML**: the new R package for automated machine learning!

Quickly generate multiple regression models with just a few lines of code, all while leveraging the powerful **tidymodels** ecosystem.

No Java setup needed! Perfect for beginners & pros alike.

Check it out! #rstats #AutoML #DataScience #tidymodels #parsnip

Introducing support for postprocessing in tidymodels!

Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations.

The tidymodels team has been working on a set of changes across many #tidymodels packages to introduce support for postprocessing. They would love to hear your thoughts on their progress so far!

Learn more in the blog post: https://www.tidyverse.org/blog/2024/10/postprocessing-preview/

#RStats

The tidymodels hex, along with two graphs representing predictions pre and post processing.

The healthyverse meta package:

healthyR: Streamline hospital data workflows
healthyR.ts: Master time series analysis
healthyR.ai: Implement AI modeling seamlessly
healthyR.data: Access curated healthcare datasets
TidyDensity: Simplify probability distributions
tidyAML: Automate machine learning with tidymodels
RandomWalker: Explore random walk analysis

install.packages("healthyverse")
library(healthyverse)

https://www.spsanderson.com/healthyverse/

#R #RStats #ML #automl #tidymodels #parsnip #randomwalks

The healthyverse meta package:

healthyR: Streamline hospital data workflows
healthyR.ts: Master time series analysis
healthyR.ai: Implement AI modeling seamlessly
healthyR.data: Access curated healthcare datasets
TidyDensity: Simplify probability distributions
tidyAML: Automate machine learning with tidymodels
RandomWalker: Explore random walk analysis

install.packages("healthyverse")
library(healthyverse)

https://www.spsanderson.com/healthyverse/

#R #RStats #ML #automl #tidymodels #parsnip #randomwalks

some more #TimeSeries testing in #r #RStats using the #tidymodels #parsnip extension #modeltime

#tidymodels

Client Info