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.
R-squared is one of the most commonly used metrics to measure performance.
But it took me 2 years to figure out the mistakes that were killing my regression models.
In 2 minutes, I'll share how I fixed 2 years of mistakes (and made 50% more accurate models than my peers). Let's go:
1. R-squared (R2):
R2 is a statistical measure used in regression models that provides a measure of how well the observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.
2. Range (0 to 1):
R2 ranges from 0 to 1. A higher R2 value indicates a better fit between the prediction and the actual data. For example, an R2 value of 0.70 suggests that 70% of the variance in the dependent variable is predictable from the independent variable(s).
Understanding probability is essential in data science.
In 4 minutes, I'll demolish your confusion.
Let's go!
1. Statistical Distributions:
There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice.
2. Discrete Distributions:
Discrete distributions are used when the data can take on only specific, distinct values. These values are often integers, like the number of sales calls made or the number of customers that converted.