Monthly Archives: February 2026

When Tree Models Actually Beat Logistic Regression (and When They Don’t)

If you work in applied data science long enough, you’ll eventually hear some version of this question:

“Should we switch from logistic regression to a tree-based model?”

Sometimes the answer is yes.
Very often, the answer is not really.

In a world filled with gradient boosting, deep learning, and AutoML tools, logistic regression can feel almost embarrassing to mention.

It’s old.
It’s simple.
It’s taught in every intro course.

Over the past decade, data scientist has become one of the most attractive titles in tech.

It promises impact, influence, and technical depth.
It suggests working on hard problems, building models, and shaping decisions with data.

Most experiment analyses start—and end—the same way.

You group by experiment variant.
You calculate averages.
You compare numbers.
You call it a day.