Advanced Artificial Intelligence Lecture 3: Decision tree
Advanced Artificial Intelligence Lecture 3: Decision Tree
Outline Introduction Constructing a decision Tree D3 C4.5 Regression Trees CART Gradient Boosting
Outline ▪ Introduction ▪ Constructing a Decision Tree ▪ ID3 ▪ C4.5 ▪ Regression Trees ▪ CART ▪ Gradient Boosting
Decision tree Introduction o The Decision Tree is one of the most powerful and popular classification and prediction algorithms in current use in data mining and machine learning The attractiveness of decision trees is due to the fact that in contrast to neural networks decision trees represent rules o Rules can readily be expressed so that humans can understand them or even directly used in a database access language like sQL so that records falling into a particular category may be retrieved
4 Decision Tree Introduction ⚫ The Decision Tree is one of the most powerful and popular classification and prediction algorithms in current use in data mining and machine learning. ⚫ The attractiveness of decision trees is due to the fact that, in contrast to neural networks, decision trees represent rules. ⚫ Rules can readily be expressed so that humans can understand them or even directly used in a database access language like SQL so that records falling into a particular category may be retrieved
Decision tree o a decision tree consists of nodes test for the value of a certain attribute Edges: correspond to the outcome of a test connect to the next node or leaf · Leaves: terminal nodes that predict the outcome
5 Decision Tree ⚫ A decision tree consists of • Nodes: test for the value of a certain attribute • Edges: correspond to the outcome of a test connect to the next node or leaf • Leaves: terminal nodes that predict the outcome
Decision tree Exampl e I SOLL IHR NEUES AUTO 1. Start at the root SEINEN PREIS WERT SEIN 2 2. Perform the test NEIN 3. Follow the edge corresponding to outcome FDMEURO 4. Go to 2 unless leaf 5. Predict that outcome associated with the leaf Genau das Wichtige
6 Decision Tree ⚫ Example 1. Start at the root 2. Perform the test 3. Follow the edge corresponding to outcome 4. Go to 2. unless leaf 5. Predict that outcome associated with the leaf