Thinking Machines: Machine Learning by Alasdair Gilchrist

Author:Alasdair Gilchrist [Gilchrist, Alasdair]
Language: eng
Format: azw3
Published: 2017-07-03T04:00:00+00:00

The way Decision Trees work

A Decision Tree builds classification or regression models in the form of a tree structure. It does this by taking a large data set and breaking it into smaller pieces or subsets that contain instances of similar values while at the same time building an incremental association tree. The final result is a tree structure with a root node and a collection of branch nodes and leaf nodes. Every decision tree is comprised of a network of nodes and each node is associated with one of the input variables. The nodes are interconnected if required by links that are called edges. Edges are associations with other nodes and edges coming from that node represent the total possible values of that node for example (true or false). Decision trees always start with a root node and end on a leaf. Trees don’t converge at any point; they split their way out from the root as the nodes are processed.

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