Decision Trees: From Hesitation to $15M Success — A Data Scientist’s Journey

Decision trees are one of those tools every data scientist should have in their toolkit. But for three years, I avoided them like the plague. I stuck to my trusty linear models, fearing tree-based models' overfitting traps and limitations. Today, I’m here to break down my journey — from hesitation to unlocking a $15M lead scoring model — and show you why decision trees (and their big brother, random forests) might change your data science game. Let’s dive in.

What’s a Decision Tree Anyway?

Imagine you’re playing a game of “20 Questions” to guess an object. You ask yes-or-no questions like, “Is it alive?” or “Does it have wheels?” Each answer narrows down the possibilities until you arrive at the final guess. That’s essentially what a decision tree does. It’s a flowchart-like structure that makes decisions by asking questions about your data.

Here’s the basic anatomy:

• Root Node: The starting point representing your entire dataset.
• Decision Nodes: Points where the data splits based on a condition (e.g., “Is age > 30?”).
• Leaf Nodes: The end of the line, where you get your answer (e.g., “Yes” or “No” for classification).