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.
K-means is one of the most powerful algorithms for data scientists.
But it's confusing for beginners. Let's fix that:
1. What is K-means?
Is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection.
2. Unsupervised:
K-means is an unsupervised algorithm that is used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset.
A new paper shows how you can predict real purchase intent without asking people.
~90% of human test–retest reliability.
Here's what's inside the 28 page paper:
1. Problem with direct Likert from LLMs:
When you ask LLMs to output 1–5 ratings directly, the distributions are too narrow/skewed and don’t look like human survey data, limiting usefulness for concept testing.
Have the LLM write a short free-text purchase-intent statement, then map that text onto a 5-point Likert score using embedding cosine similarity to predefined anchor sentences (i.e., semantic matching instead of raw numbers).
Understanding P-Values is essential for improving regression models.
In 2 minutes, I'll crush your confusion.
1. The p-value:
A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.
2. Null Hypothesis (H₀):
The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under H₀, the regressor does not affect the outcome.