Decision tree Using Machine Learning.ppt
- 2. Overview What is a Decision Tree Sample Decision Trees How to Construct a Decision Tree Problems with Decision Trees Decision Trees in Gaming Summary
- 3. An inductive learning task Use particular facts to make more generalized conclusions A predictive model based on a branching series of Boolean tests These smaller Boolean tests are less complex than a one-stage classifier Let’s look at a sample decision tree… What is a Decision Tree?
- 4. Predicting Commute Time Leave At Stall? Accident? 10 AM 9 AM 8 AM Long Long Short Medium Long No Yes No Yes If we leave at 10 AM and there are no cars stalled on the road, what will our commute time be?
- 5. Inductive Learning In this decision tree, we made a series of Boolean decisions and followed the corresponding branch Did we leave at 10 AM? Did a car stall on the road? Is there an accident on the road? By answering each of these yes/no questions, we then came to a conclusion on how long our commute might take
- 6. Decision Trees as Rules We did not have represent this tree graphically We could have represented as a set of rules. However, this may be much harder to read…
- 7. Decision Tree as a Rule Set if hour == 8am commute time = long else if hour == 9am if accident == yes commute time = long else commute time = medium else if hour == 10am if stall == yes commute time = long else commute time = short Notice that all attributes to not have to be used in each path of the decision. As we will see, all attributes may not even appear in the tree.
- 8. How to Create a Decision Tree We first make a list of attributes that we can measure These attributes (for now) must be discrete We then choose a target attribute that we want to predict Then create an experience table that lists what we have seen in the past
- 9. Sample Experience Table Example Attributes Target Hour Weather Accident Stall Commute D1 8 AM Sunny No No Long D2 8 AM Cloudy No Yes Long D3 10 AM Sunny No No Short D4 9 AM Rainy Yes No Long D5 9 AM Sunny Yes Yes Long D6 10 AM Sunny No No Short D7 10 AM Cloudy No No Short D8 9 AM Rainy No No Medium D9 9 AM Sunny Yes No Long D10 10 AM Cloudy Yes Yes Long D11 10 AM Rainy No No Short D12 8 AM Cloudy Yes No Long D13 9 AM Sunny No No Medium
- 10. Choosing Attributes The previous experience decision table showed 4 attributes: hour, weather, accident and stall But the decision tree only showed 3 attributes: hour, accident and stall Why is that?
- 11. Choosing Attributes Methods for selecting attributes (which will be described later) show that weather is not a discriminating attribute We use the principle of Occam’s Razor: Given a number of competing hypotheses, the simplest one is preferable
- 12. Choosing Attributes The basic structure of creating a decision tree is the same for most decision tree algorithms The difference lies in how we select the attributes for the tree We will focus on the ID3 algorithm developed by Ross Quinlan in 1975
- 13. Decision Tree Algorithms The basic idea behind any decision tree algorithm is as follows: Choose the best attribute(s) to split the remaining instances and make that attribute a decision node Repeat this process for recursively for each child Stop when: All the instances have the same target attribute value There are no more attributes There are no more instances
- 14. Identifying the Best Attributes Refer back to our original decision tree Leave At Stall? Accident? 10 AM 9 AM 8 AM Long Long Short Medium No Yes No Yes Long How did we know to split on leave at and then on stall and accident and not weather?
- 15. ID3 Heuristic To determine the best attribute, we look at the ID3 heuristic ID3 splits attributes based on their entropy. Entropy is the measure of disinformation…
- 16. Entropy Entropy is minimized when all values of the target attribute are the same. If we know that commute time will always be short, then entropy = 0 Entropy is maximized when there is an equal chance of all values for the target attribute (i.e. the result is random) If commute time = short in 3 instances, medium in 3 instances and long in 3 instances, entropy is maximized
- 17. Entropy Calculation of entropy Entropy(S) = ∑(i=1 to l)-|Si|/|S| * log2(|Si|/|S|) S = set of examples Si = subset of S with value vi under the target attribute l = size of the range of the target attribute
- 18. ID3 ID3 splits on attributes with the lowest entropy We calculate the entropy for all values of an attribute as the weighted sum of subset entropies as follows: ∑(i = 1 to k) |Si|/|S| Entropy(Si), where k is the range of the attribute we are testing We can also measure information gain (which is inversely proportional to entropy) as follows: Entropy(S) - ∑(i = 1 to k) |Si|/|S| Entropy(Si)
- 19. ID3 Given our commute time sample set, we can calculate the entropy of each attribute at the root node Attribute Expected Entropy Information Gain Hour 0.6511 0.768449 Weather 1.28884 0.130719 Accident 0.92307 0.496479 Stall 1.17071 0.248842
- 20. Pruning Trees There is another technique for reducing the number of attributes used in a tree - pruning Two types of pruning: Pre-pruning (forward pruning) Post-pruning (backward pruning)
- 21. Prepruning In prepruning, we decide during the building process when to stop adding attributes (possibly based on their information gain) However, this may be problematic – Why? Sometimes attributes individually do not contribute much to a decision, but combined, they may have a significant impact
- 22. Postpruning Postpruning waits until the full decision tree has built and then prunes the attributes Two techniques: Subtree Replacement Subtree Raising
- 23. Subtree Replacement Entire subtree is replaced by a single leaf node A B C 1 2 3 4 5
- 24. Subtree Replacement Node 6 replaced the subtree Generalizes tree a little more, but may increase accuracy A B 6 4 5
- 25. Subtree Raising Entire subtree is raised onto another node A B C 1 2 3 4 5
- 26. Subtree Raising Entire subtree is raised onto another node This was not discussed in detail as it is not clear whether this is really worthwhile (as it is very time consuming) A C 1 2 3
- 27. Problems with ID3 ID3 is not optimal Uses expected entropy reduction, not actual reduction Must use discrete (or discretized) attributes What if we left for work at 9:30 AM? We could break down the attributes into smaller values…
- 28. Problems with Decision Trees While decision trees classify quickly, the time for building a tree may be higher than another type of classifier Decision trees suffer from a problem of errors propagating throughout a tree A very serious problem as the number of classes increases
- 29. Error Propagation Since decision trees work by a series of local decisions, what happens when one of these local decisions is wrong? Every decision from that point on may be wrong We may never return to the correct path of the tree
- 31. Problems with ID3 If we broke down leave time to the minute, we might get something like this: 8:02 AM 10:02 AM 8:03 AM 9:09 AM 9:05 AM 9:07 AM Long Medium Short Long Long Short Since entropy is very low for each branch, we have n branches with n leaves. This would not be helpful for predictive modeling.
- 32. Problems with ID3 We can use a technique known as discretization We choose cut points, such as 9AM for splitting continuous attributes These cut points generally lie in a subset of boundary points, such that a boundary point is where two adjacent instances in a sorted list have different target value attributes
- 33. Problems with ID3 Consider the attribute commute time 8:00 (L), 8:02 (L), 8:07 (M), 9:00 (S), 9:20 (S), 9:25 (S), 10:00 (S), 10:02 (M) When we split on these attributes, we increase the entropy so we don’t have a decision tree with the same number of cut points as leaves
- 34. ID3 in Gaming Black & White, developed by Lionhead Studios, and released in 2001 used ID3 Used to predict a player’s reaction to a certain creature’s action In this model, a greater feedback value means the creature should attack
- 35. ID3 in Black & White Example Attributes Target Allegiance Defense Tribe Feedback D1 Friendly Weak Celtic -1.0 D2 Enemy Weak Celtic 0.4 D3 Friendly Strong Norse -1.0 D4 Enemy Strong Norse -0.2 D5 Friendly Weak Greek -1.0 D6 Enemy Medium Greek 0.2 D7 Enemy Strong Greek -0.4 D8 Enemy Medium Aztec 0.0 D9 Friendly Weak Aztec -1.0
- 36. ID3 in Black & White Allegiance Defense Friendly Enemy 0.4 -0.3 -1.0 Weak Strong 0.1 Medium Note that this decision tree does not even use the tribe attribute
- 37. ID3 in Black & White Now suppose we don’t want the entire decision tree, but we just want the 2 highest feedback values We can create a Boolean expressions, such as ((Allegiance = Enemy) ^ (Defense = Weak)) v ((Allegiance = Enemy) ^ (Defense = Medium))
- 38. Summary Decision trees can be used to help predict the future The trees are easy to understand Decision trees work more efficiently with discrete attributes The trees may suffer from error propagation