Stat Mills

In this post I’ll walk through how you can use the same principles behind Gradient Boosting Machines (GBM) to predict parameters of models in the same way traditional GBMs predict 1-dimensional targets. This allows us to get the benefits of Gradient Boosting (large feature sets, high dimensional interactions, modeling at... [Read More]

I’ve worked in Statistics, Data Science, and Machine Learning for 12 years and like most other Data Scientists I’ve been thinking about how LLMs impact my workflow and my career. The more my job becomes asking an AI to accomplish tasks, the more I worry about getting called in to... [Read More]

Let’s say you have a trend you are trying to model that you know to be monotonically increasing or decreasing; this could be something like default as a function of risk, power usage as a function of temperature, or CO2 emissions over time. Generalized Additive Models (GAMs) are a great... [Read More]

I recently found out that the Federal Housing Authority publishes a ton of granular housing data and wanted to start exploring the data for Atlanta, where I live. I’m not sure there will be anything revelatory in here, but sometimes it’s fun to look at a map. FHA Public Use... [Read More]

Sometimes in my Data Science projects I need a quick and easy way to measure and visualize a predictive trend for several independent variables. My go to way to do this is to use Weight of Evidence and Information Value. Recently I put together some code in python to calculate... [Read More]