data tree as a way to talk about problem data

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Things to think about with trees: Fundamental concepts on data-partitioning, recursive binary splitting, and nodes etc.
Data exploration and data preparation for building classification models
Performance metrics for decision tree models - Gini Index, Entropy, and Classification Error.
https://link.springer.com/article/10.1007/s10994-017-5633-9
https://www-users.cs.umn.edu/~kumar001/dmbook/ch4.pdf
Basically, Classification Trees are a hierarchical way of partitioning the space.
A Decision Tree is a simple representation for classifying examples. It is a Supervised Machine Learning where the data is continuously split according to a certain parameter.
Decision Tree consists of :
Nodes : Test for the value of a certain attribute.
Edges/ Branch : Correspond to the outcome of a test and connect to the next node or leaf.
Leaf nodes : Terminal nodes that predict the outcome (represent class labels or class distribution)

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how policing data relates internally and externally