Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes by Roger Barga & Valentine Fontama & Wee Hyong Tok

Author:Roger Barga & Valentine Fontama & Wee Hyong Tok [Barga, Roger]
Language: eng
Format: epub, azw3, mobi
ISBN: 9781484204467
Publisher: Apress
Published: 2014-11-21T08:00:00+00:00

Decision Trees

Decision tree algorithms are hierarchical techniques that work by splitting the dataset iteratively based on certain statistical criteria. The goal of decision trees is to maximize the variance across different nodes in the tree, and minimize the variance within each node. Figure 4-3 shows a simple decision tree created with two splits of the data. The root node (Node 0) contains all the data in the dataset. The algorithm splits the data based on a defined statistic, creating three new nodes (Node 1, Node 2, and Node 3). Using the same statistic, it splits the data again at Node 1, creating two more leaf nodes (i.e. Nodes 4 and 5). The decision tree makes its prediction for each data row by traversing to the leaf nodes (i.e. one of the terminal nodes: Node 2, 3, 4, or 5).

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