Neura saysDecision trees split data on feature thresholds — interpretable but prone to overfitting.
A tree asks a series of yes/no questions ("age > 30?"), splitting data to maximize purity (measured by Gini or entropy) until leaves are mostly one class. They're highly interpretable (you can read the rules) and handle mixed feature types, but a deep tree memorizes the training data (overfits). Pruning and depth limits help — and ensembles fix it entirely.
Power-ups you unlock
Yes/no splits maximizing purity (Gini/entropy)
Highly interpretable — readable rules
Handle mixed feature types
Overfit easily; prune/limit depth
The Overfit Ogre attacks — common mistakes
Letting trees grow unbounded (overfit)
Reading one deep tree as stable truth
Ignoring that small data changes reshape the tree
Boss battleExplain why a deep, unpruned tree overfits.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>age > 30?
├ yes → income > 50k? → ...
└ no → ...
deep tree → memorizes training data → overfit</pre></body></html>