Decision Tree Induction

After learning some basics about Machine Learning (ML), time to get into the details related to my thesis. After discussing with my supervisors, we decided to implement classification algorithm based on decision tree. So, in this post, I would like to give an overview about decision-tree in ML.

An example of decision tree from XKCD
An example of decision tree from XKCD 😉

What is decision-tree?

Decision-tree is the common output of a divide-and-conquer approach in learning from a set of independent instances. A decision tree consists of nodes and branches. Each node consists of questions based on one or several attributes i.e. compares an attribute value with a constant or it could compare more than one attributes using some functions. Learning data set to produce a decision tree is often called tree-induction. Continue reading Decision Tree Induction