# Decision Tree Visualization and Interpretation

I have a question on Decision tree visualization.

I have used scikit-learn Decision Tree classifier for my analysis.

I have 2 classes to predict: 0 and 1 (it comes up as a numeric field when I load the dataset)

I have given the class_names as "NotPresent" and "Ispresent" which I believe it will map to 0 and 1. is that correct?

How do I interpret the nodes and value present in each nodes in the accompanying diagram? What do the value matrix represent or calculate?

I would recommend to read a few tutorials such as this one. At a general level, GINI metric that the algorithm computes to decide whether to split. The variable name and comparison (<,>, etc) and value in a node represent on what information you should travel on the left side of the node or right side. The class is the most frequently occurring class in your tree.