in this illustrationeach node in the tree either represents question or terminal node (also called leafthat contains the answer the edges connect the answers to question with the next question you would ask in machine learning parlancewe built model to distinguish between four classes of animals (hawkspenguinsdolphinsand bearsusing the three features "has feathers,"can fly,and "has fins instead of building these models by handwe can learn them from data using supervised learning building decision trees let' go through the process of building decision tree for the classification dataset shown in figure - the dataset consists of two half-moon shapeswith each class consisting of data points we will refer to this dataset as two_moons learning decision tree means learning the sequence of if/else questions that gets us to the true answer most quickly in the machine learning settingthese questions are called tests (not to be confused with the test setwhich is the data we use to test to see how generalizable our model isusually data does not come in the form of binary yes/no features as in the animal examplebut is instead represented as continuous features such as in the dataset shown in figure - the tests that are used on continuous data are of the form "is feature larger than value ?supervised machine learning algorithms