Julia Silge

Walk through a tidymodels analysis from beginning to end to predict whether water is available at a water source in Sierra Leone.

Are more CEO departures involuntary now than in the past?

Use tidymodels to build features for modeling from Netflix description text, then fit and evaluate a support vector machine model.

Use tidymodels to predict post office location with subword features and a support vector machine model.

Explore country-level UN voting with a tidymodels approach to unsupervised machine learning.

Estimate how commercial characteristics like humor and patriotic themes change with time using tidymodels functions for bootstrap confidence intervals.

Use tidy data principles to understand which kinds of occupations are most similar in terms of demographic characteristics.

Explore results of models with convenient tidymodels functions.

I am happy to announce that this free, open source, interactive course on text mining with tidy data principles is now published!

Check residuals and other model diagnostics for regression models trained on text features, all with tidymodels functions.

Download up-to-date city data from Chicago’s open data portal and predict whether a traffic crash involved an injury with a bagged tree model.

The current development version of tidytext has changes that may affect your analyses.

Use tidymodels scaffolding functions for getting started quickly with commonly used models like random forests.

Use tidymodels to predict capacity for Canadian wind turbines with decision trees.

Which of the Datasaurus Dozen are easier or harder for a random forest model to identify?

Tune a hyperparameter and then understand how to choose the best value afterward, using tidymodels for modeling the relationship between expected wins and tournament seed.

Use tidymodels for feature engineering steps like imputing missing data and subsampling for class imbalance, and build predictive models to predict the probability of survival for Himalayan climbers.

An initial version of the first eleven chapters are available today!

Learn how to use tidyverse and tidymodels functions to fit and analyze many models at once.

Use text features and tidymodels to predict the speaker of individual lines from the show, and learn how to compute model-agnostic variable importance for any kind of model.

Build two kinds of classification models and evaluate them using resampling.

Announcing our new book, to be published in the Chapman & Hall/CRC Data Science Series!

Learn how to use bootstrap aggregating to predict the duration of astronaut missions.

Explore data from the Claremont Run Project on Uncanny X-Men with bootstrap resampling.

Understand more about the forced transport of African people using the Slave Voyages database.

Use tidymodels for unsupervised dimensionality reduction.

Measure how the frequency of some feature differs across some group or set, using the weighted log odds.

Learn how to tune hyperparameters for an XGBoost classification model to predict wins and losses.

I am happy to announce that a new version of my free, online, interactive course has been published!

Lately I’ve been publishing screencasts demonstrating how to use the tidymodels framework, from first steps in modeling to how to evaluate complex models.

A lot has been happening in the tidymodels ecosystem lately!

This is an exciting week for us on the tidymodels team; we launched tidymodels.org, a new central location with resources and documentation for tidymodels packages.

Lately I’ve been publishing screencasts demonstrating how to use the tidymodels framework, from first steps in modeling to how to tune more complex models.

I’ve been publishing screencasts demonstrating how to use the tidymodels framework, from first steps in modeling to how to tune more complex models.

I’ve been publishing screencasts demonstrating how to use the tidymodels framework, from first steps in modeling to how to tune more complex models.

I’ve been publishing screencasts demonstrating how to use the tidymodels framework, from first steps in modeling to how to tune more complex models.

I’ve been publishing screencasts demonstrating how to use the tidymodels framework, from first getting started to how to tune machine learning models.

Last week I published a screencast demonstrating how to use the tidymodels framework and specifically the recipes package.

Last week I published my first screencast showing how to use the tidymodels framework for machine learning and modeling in R.

This week I started my new job as a software engineer at RStudio, working with Max Kuhn and other folks on tidymodels.

One of the biggest projects I have worked on over the past several years is the Stack Overflow Developer Survey, and one of the most unique aspects of this survey is the extensive salary data that is collected.

A paper I worked on was just published in a medical journal.

Today I am happy to announce a new(-ish), free, online, interactive course that I have developed, Supervised Machine Learning: Case Studies in R!

I am so pleased to now be an RStudio-certified tidyverse trainer!

Today I am so pleased to introduce a new package for calculating weighted log odds ratios, tidylo.

I recently wrote about the release of tidytext 0.2.1, and one of the most useful new features in this release is a couple of helper functions for making plots with ggplot2.

Today tidytext 0.2.1 is available on CRAN!

Note: cross-posted with the rOpenSci blog. rOpenSci is one of the first organizations in the R community I ever interacted with, when I participated in the 2016 rOpenSci unconf.

Since 2017 I have been an instructor for DataCamp, the VC-backed online data science education platform.

In the most recent issue of the R Journal, I have a new paper out with coauthors John Nash and Spencer Graves.