Tree-based predictive models that recursively partition feature space, including split criteria, complexity control, pruning, and interpretation.
Tagged articles
11 articles in this tag.
A fully grown decision tree can score near-perfect on the data it was trained on and still lose to a single split on data it has never seen. The usual…
Read tutorial
Your decision tree scores 98% on training data. On new data, it drops to 74%. The tree didn't fail because it wasn't smart enough. It failed because it was…
Read tutorial
A decision tree can score 100% on the data it was trained on and still be wrong about almost everything else. That gap is not a bug. It is the lesson.
Read tutorial
You fit a tree, print it, and there it is: the root splits on worst radius, not mean texture. The library made a choice. You can't say why.
Read tutorial
A decision tree is not a rulebook the model memorizes. It is a partitioning machine: a greedy process that keeps cutting your data into smaller, cleaner…
Read tutorial
"The model says education matters" sounds like a complete explanation. It is not. It is the beginning of a much narrower claim: the model leaned on…
Read tutorial
You have probably heard the advice: "Just use a random forest. Trees handle everything." It sounds practical. It is also how many beginners end up with a…
Read tutorial
A single decision tree can feel like a confident liar: it nails your training data, then wobbles on new data, and reshapes its entire structure when one…
Read tutorial
"Extra Trees" does not mean more trees. It means extra randomness — and that single distinction changes how the ensemble learns, how fast it trains, and…
Read tutorial
A classification tree asks which split makes the labels purest. A regression tree has no labels to purify — only numbers that scatter. So what is it…
Read tutorial
Two candidate splits sit in front of you. One carves the data into two clean, balanced groups. The other produces a lopsided partition that looks worse by…
Read tutorial