Imagine we ve got a set of data containing several types, or classes. E.g. information about customers, and class=whether or not they buy anything.
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1 Decision Trees
2 Defining the Task Imagine we ve got a set of data containing several types, or classes. E.g. information about customers, and class=whether or not they buy anything. Can we predict, i.e classify, whether a previously unseen customer will buy something?
3 An Example Decision Tree Attribute n v n1 v n2 v n3 Attribute m Attribute k v m1 v m2 Class 1 v k1 v k2 Attribute l v l1 v l2 Class 2 Class 1 Class 2 Class 2 Class 1 We create a decision tree. It acts like a function that can predict and output given an input
4 Decision Trees The idea is to ask a series of questions, starting at the root, that will lead to a leaf node. The leaf node provides the classification.
5 Decision Trees Consider these data: A number of examples of weather, for several days, with a classification PlayTennis.
6 Decision Tree Algorithm Building a decision tree 1. Select an attribute 2. Create the subsets of the example data for each value of the attribute 3. For each subset if not all the elements of the subset belongs to same class repeat the steps 1-3 for the subset
7 Building Decision Trees Let s start building the tree from scratch. We first need to decide which attribute to make a decision. Let s say we selected humidity Humidity D1,D2,D3,D4 D8,D12,D14 high normal D5,D6,D7,D9 D10,D11,D13
8 Building Decision Trees w lets classify the first subset D1,D2,D3,D4,D8,D12,D14 using attribute wind Humidity D1,D2,D3,D4 D8,D12,D14 high normal D5,D6,D7,D9 D10,D11,D13
9 Building Decision Trees Subset D1,D2,D3,D4,D8,D12,D14 classified by attribute wind Humidity high normal strong wind weak D5,D6,D7,D9 D10,D11,D13 D2,D12,D14 D1,D3,D4,D8
10 Building Decision Trees w lets classify the subset D2,D12,D14 using attribute Humidity high normal strong wind weak D5,D6,D7,D9 D10,D11,D13 D2,D12,D14 D1,D3,D4,D8
11 Building Decision Trees Subset D2,D12,D14 classified by Humidity high normal strong wind weak D5,D6,D7,D9 D10,D11,D13 D2,D12,D14 D1,D3,D4,D8
12 Building Decision Trees subset D2,D12,D14 classified using attribute Humidity high normal strong wind weak D5,D6,D7,D9 D10,D11,D13 D1,D3,D4,D8 Sunny Rain Overcast
13 Building Decision Trees w lets classify the subset D1,D3,D4,D8 using attribute Humidity high normal strong wind weak D5,D6,D7,D9 D10,D11,D13 D1,D3,D4,D8 Sunny Rain Overcast
14 Building Decision Trees subset D1,D3,D4,D8 classified by Humidity high normal strong wind weak D5,D6,D7,D9 D10,D11,D13 Sunny Rain Overcast Sunny Rain Overcast
15 Building Decision Trees w classify the subset D5,D6,D7,D9,D10,D11,D13 using attribute Humidity high normal strong wind weak D5,D6,D7,D9 D10,D11,D13 Sunny Rain Overcast Sunny Rain Overcast
16 Building Decision Trees subset D5,D6,D7,D9,D10,D11,D13 classified by Humidity high normal wind strong weak Sunny Rain Overcast Sunny Rain Overcast D5,D6,D10 Sunny Rain Overcast
17 Building Decision Trees Finally classify subset D5,D6,D10by wind Humidity high normal wind strong weak Sunny Rain Overcast Sunny Rain Overcast D5,D6,D10 Sunny Rain Overcast
18 Building Decision Trees subset D5,D6,D10 classified by wind Humidity high normal wind strong weak Sunny Rain Overcast wind Sunny Rain Overcast Sunny Rain Overcast strong weak
19 Decision Trees and Logic The decision tree can be expressed as an expression or if-then-else sentences: (humidity=high wind=strong =overcast) (humidity=high wind=weak =overcast) (humidity=normal =sunny) (humidity=normal =overcast) (humidity=normal =rain wind=weak) Humidity high normal wind strong weak Sunny Rain Overcast wind Sunny Rain Overcast Sunny Rain Overcast strong weak
20 Using Decision Trees w let s classify an unseen example: <sunny,hot,normal,weak>=? Humidity high normal wind strong weak Sunny Rain Overcast wind Sunny Rain Overcast Sunny Rain Overcast strong weak
21 Using Decision Trees Classifying: <sunny,hot,normal,weak>=? Humidity high normal wind strong weak Sunny Rain Overcast wind Sunny Rain Overcast Sunny Rain Overcast strong weak
22 Using Decision Trees Classification for: <sunny,hot,normal,weak>= Humidity high normal wind strong weak Sunny Rain Overcast wind Sunny Rain Overcast Sunny Rain Overcast strong weak
23 A Big Problem Here s another tree from the same training data that has a different attribute order: Which attribute should we choose for each branch?
24 Choosing Attributes We need a way of choosing the best attribute each time we add a node to the tree. Most commonly we use a measure called entropy. Entropy measure the degree of disorder in a set of objects.
25 Entropy In our system we have 9 positive examples 5 negative examples The entropy, E(S), of a set of examples is: c E(S) = Σ-p i log p i i=1 Where c = no of classes and p i = ratio of the number of examples of this value over the total number of examples. P+ = 9/14 P- = 5/14 E = - 9/14 log 2 9/14-5/14 log 2 5/14 E = In a homogenous (totally ordered) system, the entropy is 0. - In a totally heterogeneous system (totally disordered), all classes have equal numbers of instances; the entropy is 1
26 Entropy We can evaluate each attribute for their entropy. E.g. evaluate the attribute Temperature Three values: Hot, Mild, Cool. So we have three subsets, one for each value of Temperature. S hot ={D1,D2,D3,D13} S mild ={D4,D8,D10,D11,D12,D14} S cool ={D5,D6,D7,D9} We will now find: E(S hot ) E(S mild ) E(S cool )
27 Entropy S hot = {D1,D2,D3,D13} Examples: 2 positive 2 negative Totally heterogeneous + disordered therefore: p + = 0.5 p - = 0.5 Entropy(S hot ),= -0.5log log = 1.0 S mild = {D4,D8,D10, D11,D12,D14} Examples: 4 positive 2 negative Proportions of each class in this subset: p + = p - = Entropy(S mild ),= log log = S cool ={D5,D6,D7,D9} Examples: 3 positive 1 negative Proportions of each class in this subset: p + = 0.75 p - = 0.25 Entropy(S cool ),= -0.25log log = 0.811
28 Gain w we can compare the entropy of the system before we divided it into subsets using Temperature, with the entropy of the system afterwards. This will tell us how good Temperature is as an attribute. The entropy of the system after we use attribute Temperature is: ( S hot / S )*E(S hot ) + ( S mild / S )*E(S mild ) + ( S cool / S )*E(S cool ) (4/14)*1.0 + (6/14)* (4/14)*0.811 = This difference between the entropy of the system before and after the split into subsets is called the gain: E(before) E(afterwards) Gain(S,Temperature) = = 0.029
29 Decreasing Entropy From the initial state, Where there is total disorder 7red class 7pink class: E=1.0 Has a cross? no yes Both subsets E=-2/7log2/7 5/7log5/7 to the final state where all subsets contain a single class Has a ring? Has a ring? no yes no yes All subset: E=0.0
30 Tabulating the Possibilities Attribute=value + - E E after dividing by attribute A Gain Outlook=sunny 2 3-2/5 log 2/5 3/5 log 3/5 = Outlook=o cast 4 0-4/4 log 4/4 0/4 log 0/4 = 0.0 Outlook=rain 3 2-3/5 log 3/5 2/5 log 2/5 = Temp =hot 2 2-2/2 log 2/2 2/2 log 2/2 = Temp =mild 4 2-4/6 log 4/6 2/6 log 2/6 = Temp =cool 3 1-3/4 log 3/4 1/4 log 1/4 = Etc etc This shows the entropy calculations
31 Table continued E for each subset of A Weight by proportion of total E after A is the sum of the weighted values Gain = (E before dividing by A) (E after A) -2/5 log 2/5 3/5 log 3/5 = /4 log 4/4 0/4 log 0/4 = 0.0-3/5 log 3/5 2/5 log 2/5 = /2 log 2/2 2/2 log 2/2 = 1.0-4/6 log 4/6 2/6 log 2/6 = /4 log 3/4 1/4 log 1/4 = x 5/14 = x 4/14 = x 5/14 = x 4/14 = x 6/14 = x 4/14 = and this shows the gain calculations
32 Gain We calculate the gain for all the attributes. Gain(S,Outlook) = Gain(S,Humidity) = Gain(S,Wind) = Gain(S, Temp ) = Then we see which of them will bring more order to the set of examples. The first node in the tree should be the one with the highest value, i.e. Outlook.
33 ID3 (Decision Tree Algorithm) ID3 was the first proper decision tree algorithm to use this mechanism: Building a decision tree with ID3 algorithm 1. Select the attribute with the most gain 2. Create the subsets for each value of the attribute 3. For each subset 1. if not all the elements of the subset belongs to same class repeat the steps 1-3 for the subset Or in more detail
34 ID3 (Decision Tree Algorithm) Function DecisionTtreeLearner(Examples, TargetClass, Attributes) create a Root node for the tree if all Examples are positive, return the single-node tree Root, with label = if all Examples are negative, return the single-node tree Root, with label = if Attributes list is empty, return the single-node tree Root, with label = most common value of TargetClass in Examples else A = the attribute from Attributes with the highest information gain with respect to Examples Make A the decision attribute for Root for each possible value v of A: add a new tree branch below Root, corresponding to the test A = v let Examplesv be the subset of Examples that have value v for attribute A if Examplesv is empty then add a leaf node below this new branch with label = most common value of TargetClass in Examples else add the subtree DTL(Examplesv, TargetClass, Attributes - { A }) end if end return Root
35 Hypothesis Space ID3 is a heuristic to select a tree in top-down fashion. It s a form of Bestfirst search!
36 The Problem of Overfitting Trees may grow to include irrelevant attributes (e.g. color of dice). ise may add spurious nodes to the tree. This can cause overfitting of the training data relative to test data. Hypothesis H overfits the data if there exists H with greater error than H, over training examples, but less error than H over entire distribution of instances.
37 Fixing Overfitting Two approaches to pruning Prepruning: Stop growing tree during the training when it is determined that there is not enough data to make reliable choices. Postpruning: Grow whole tree but then remove the branches that do not contribute good overall performance. Determining which branch to prune Cross-validation: Reserve some of the training data(validation set) to evaluate the performance of the tree. Statistical testing: Use a statistical test (chi-square) to determine if going below a branch is likely to produces an improvement behind the training set ( a good explanation of chi-square is at. Minimum Description Length: Use an explicit measure (MDL) to determine if the additional complexity of the hypothesis is less complex than just remembering any exceptions.
38 Reduced-Error Pruning Reduced-error pruning 1. divide data into training set and validation set 2. build a decision tree using the training set, allowing overfitting to occur 3. for each node in tree consider effect of removing subtree rooted at node, making it a leaf, and assigning it majority classification for examples at that node note performance of pruned tree over validation set 4. remove node that most improves accuracy over validation set 5. repeat steps 3 and 4 until further pruning is harmful
39 Rule Post-Pruning Rule post-pruning infer decision tree, allowing overfitting to occur convert tree to a set of rules (one for each path in tree) prune (generalize) each rule by removing any preconditions (i.e., attribute tests) that result in improving its accuracy over the validation set sort pruned rules by accuracy, and consider them in this order when classifying subsequent instances IF (Outlook = Sunny) ^ (Humidity = High) THEN PlayTennis = Try removing (Outlook = Sunny) condition or (Humidity = High) condition from the rule and select whichever pruning step leads to the biggest improvement in accuracy on the validation set (or else neither if no improvement results). advantages of rule representation: each path's constraints can be pruned independently of other paths in the tree removes distinction between attribute tests that occur near the root and those that occur later in the tree converting to rules improves readability
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