The capacity of each node (how many points it can hold before splitting) controls the shape of the tree. A low capacity means nodes split early, producing a deep tree with many small cells. A high capacity means nodes tolerate more points before splitting, producing a shallow tree with larger cells.
One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.
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