Hash map instead of ladders

Cool tip from Adrian Olszewski about avoiding the “anti-pattern” of a ladder of if then else statements. Posting here to remind me to try that out when I have a chance.

Tidymodels site launch

Max Kuhn, author of the caret package (Classification And REgression Training), the indispensible and universally acclaimed R toolbox for machine learning, announced yesterday (April 21, 2020) the launch of tidymodels.org. The site is a central location for learning and using the tidymodels framework, a collection of packages for modeling and machine learning based on Tidyverse principles. I’m headed over there now for the Getting Started articles. It’s time to ramp up my machine learning skills on the new framework.

BMI, Exercise, and Diabetes

Introduction This analysis illustrates logistic regression. On GitHub. Diabetes is one of the most common and costly chronic diseases. An estimated 23.1 million people in the United States are diagnosed with diabetes at a cost of more than $245 billion per year. (National Diabetes Statistics Report, 2017. Centers for Disease Control and Prevention. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf) In this report I consider the question, are exercise level and body mass index predictive of diabetes?

A post about posts

Static A static website is a convenient platform for a blog. It serves fast, and it’s easy to maintain (if there are no bugs). Hugo makes it easy to generate pages. The blogdown R package makes it easy to author content in Markdown. If you maintain it on GitHub, you have an effective collaboration tool. Netlify hosting gets you automated deployment from a GitHub repo. A beginning is a delicate time Here’s how our team sets up to publish together.

Hello, Machine Learning

Introduction This is a Hello, World type of project for getting acquainted with machine learning. It examines the iris dataset. References: Your First Machine Learning Project in R Step-By-Step. The caret package. Package home page by Max Kuhn. Approach I began with this outline. Define problem. Prepare data. Evaluate algorithms. Improve results. Present results. Structured write up I revised the presentation per these questions, suggested by Data Science Weekly.