Summary
Well, this has been a very busy chapter. We covered the uses of decision trees for a variety of applications. The basic decision tree has leaves (nodes) and links, or branches, that each represent a decision or a change in a path. We learned about fishbone diagrams and root cause analysis, a special type of decision tree. We showed a method using scikit-learn to have the computer build a classification decision tree for us and create a usable graph. We discussed the concept of random forests, which are just an evolved form of using groups of decision trees to perform prediction or regression. Then, we got into graph search algorithms and path planners, spending some time on the A* (or A-star) algorithm, which is widely used for making routes and paths. For times when we do not have a map created in advance, the D* (or dynamic A-star) process can use dynamic replanning to continually adjust the robot’s path to reach its goal. Finally, we introduced topological graph path...
