Mark H. White II, PhD

March 9th, 2026 This is definitely the most interesting year that I’ve done this forecast.

I wrote a post last year looking at how to employ tools in LangChain to have GPT-3.5 Turbo access information on the web, outside of its training data.

I wrote a post in July 2023 describing my process for building a supervised text classification pipeline.

February 23rd, 2025 After the SAG and ASC awards last night, we’ve reached the end of the schedule: The only thing left is the Academy Awards, coming up this Sunday.

Following my previous post, I am again looking at how to employ R and Python seamlessly to use large language models (LLMs).

Well, it looks like the time has finally come for me to join the club and write a large language model (LLM) blog post.

The Academy Awards are a week away, and I’m sharing my machine-learning-based predictions for Best Picture as well as some insights I took away from the process (particularly XGBoost’s sparsity-aware split finding).

Many organizations have a substantial amount of human-generated text from which they are not extracting a proportional amount of insight.

I started taking photos earlier this year.

I like visual arts, but I’m moderately colorblind and thus have never been great at making my own works.

One of the main assumptions of linear regression taught in statistics courses is that of “constant variance” or “homoscedasticity.”

I set out to do two things with this post: Show how one can use workflowsets in the tidymodels context to compare different pre-processing and model types against one another.

During the lockdown of the city, game nights are happening on Zoom a lot.

This week was the week of April 14th, the date that shares the (French) name of Aphex Twin’s most well-known work, “Avril 14th.”

The t-test is one of the most often used in psychology and other social sciences.

Case 1 best-worst scaling (also known as MaxDiff) designs involve presenting respondents with a number of items and asking them to pick which is “best” and “worst” of the set.

Simulating data is an invaluable tool.

Welcome from Part 1, where I talked mainly about methods; Part 2, where I discussed the three major types of Star Wars fans; Part 3, where I discussed sexism and political attitudes; and Part 4, where I discussed age and nostalgia.

Welcome from Part 1, where I talked mainly about methods; Part 2, where I discussed the three major types of Star Wars fans; and Part 3, where I discussed sexism and political attitudes.

Welcome from Part 1, where I talked mainly about methods, and Part 2, where I discussed the three major types of Star Wars fans.