Just 3 days ago, I had the pleasure of watching the #rstudioconf2022 kick off.
I've been attending since 2018 and watching even longer than that.
And, I was just a normal spectator in the audience until this happened.
@topepos and @juliasilge's keynote showed all of the open source work their team has been working on to build the best machine learning ecosystem in R called #tidymodels.
And then they brought this slide up.
Max and Julia then proceeded to talk about how the community members have been working on expanding the ecosystem.
- Text Recipes for Text
- Censored for Survival Modeling
- Stacks for Ensembles
And then they announced me and my work on Modeltime for Time Series!!!
I had no clue this was going to happen.
Just a spectator in the back.
My friends to both sides went nuts. Hugs, high-fives, and all.
My students in my slack channel went even more nuts.
Throughout the rest of the week, I was on cloud-9.
My students that were at the conf introduced themselves.
Much of our discussions centered around Max & Julia's keynote and the exposure that modeltime got.
And all of this wouldn't be possible without the support of this company. Rstudio / posit.
So, I'm honored to be part of something bigger than just a programming language.
And if you'd like to learn more about what I do, I'll share a few links.
The first is my modeltime package for #timeseries.
This has been a 2-year+ passion project for building the premier time series forecasting system.
It now has multiple extensions including ensembles, resampling, deep learning, and more.
Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.
Here’s what it does: 🧵
What it does:
→ Extracts structured data from messy text
→ Grounds every field to the exact source location
→ Handles 100+ page docs
→ Generates interactive HTML for verification
→ Works with Gemini + local models
What it replaces:
→ Regex/fragile parsing
→ Custom NER pipelines
→ Expensive extraction APIs
→ Manual data entry
Understanding regression models is essential in data science.
In 4 minutes, I'll demolish your confusion. Let's go:
1. The 6 Diagnostic Checks Every Data Scientist Should Run
Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.
2. Posterior Predictive Check
Ask yourself: do the model-predicted lines resemble the observed data line? If your model is a good fit, simulated data from it should look similar to your actual data. When they diverge wildly, your model is missing something important.
Understanding regression models is essential in data science.
In 4 minutes, I'll demolish your confusion. Let's go:
1. The 6 Diagnostic Checks Every Data Scientist Should Run
Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.
2. Posterior Predictive Check
Ask yourself: do the model-predicted lines resemble the observed data line? If your model is a good fit, simulated data from it should look similar to your actual data. When they diverge wildly, your model is missing something important.