Decision Trees

A Decision Tree is a visual and intuitive machine learning method that makes decisions by asking questions about the data. It works like a flowchart, starting with a question at the top and branching out based on answers. These questions are not just yes/no, but can also be comparisons, like "Is the age greater than 18?" or "Is the temperature below 30°C?" Each split is chosen using a quantitative measure, such as information gain or Gini impurity, to find the best threshold for separating the data.

Exercicio: AI Unplugged 1

You will form teams of two and use the training data to develop criteria for distinguishing biting from non-biting monkeys. These must be clearly noted so that they can be applied to new examples by another team afterwards. A possibility to record the criteria is a decision tree. It should be the goal that the existence or absence of a particular feature permits a clear assignment to one of the groups. The use of decision trees is optional, alternatively, it is also possible to explicitly write down decision rules.

At the end ofthe training phase, the criteria formulated are exchanged with another team. Now, the students are shown the pictures of the remaining monkeys (test data) one after the other. For each image, the teams decide whether the monkey will bite or not using the scheme of rules developed by their classmates...