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Climbing trees 1: what are decision trees?

This is the first in a series of posts about decision trees in the context of machine learning. The goal here is to provide a foundational understanding of decision trees and to implement them.

I’ve drawn upon var­i­ous sources in­stru­men­tal to my un­der­stand­ing of de­ci­sion trees, in­clud­ing books, doc­u­men­ta­tion, ar­ti­cles, blog posts and lec­tures. Still, as di­men­sion­al­ity grows and the data be­comes sparse, it be­comes harder to find mean­ing­ful splits with­out cap­tur­ing noise. When a de­ci­sion tree en­coun­ters a data point where one or more fea­ture fall out­side the bounds of what it was trained on, it sim­ply as­signs the value of the near­est leaf node.

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