The 15-Day AI/ML Interview Bootcamp
Day 6 — Decision trees, random forests, and gradient boosting
Grow a decision tree from scratch with Gini impurity and information gain, see why a single tree overfits, then fix it two ways — bagging into a random forest and boosting into gradient-boosted trees — the ensemble questions every machine learning interview asks.
Your card was declined at a café in Lisbon this morning. Somewhere a model asked three questions — is this country new for the card? is the amount above the usual? was there another payment less than an hour ago? — and on the third yes it said no. When you rang the bank, the person on the line walked through the same three questions out loud, and you could follow every one. That is a decision tree: a model you can read as a flowchart, that never asked you to scale a column, and that a human can argue with. It is also, on its own, the most overconfident model in this course. Last week you told …
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