Induction on Decision Trees
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1 Séance «IDT» de l'ue «apprentissage automatique» Bruno Bouzy
2 Outline Induction task ID3 Entropy (disorder) minimization Noise Unknown attribute values Selection criterion
3 The induction task Formalism: objects with attributes Example: objects = saturday mornings attributes: outlook {sunny, overcast, rain} temperature {cool, mild, hot} humidity {high, normal} windy {true, false}
4 The induction task One particular saturday: Outlook = overcast Temperature = cool Humidity = normal Windy = false Classes mutually exclusive, here 2 classes: Positive (P) Negative (N)
5 The induction task Training set: objects whose class is known Goal: Develop a classification rule
6 A small training set n outlook temperat. humidity windy C 1 sunny hot high false N 2 sunny hot high true N 3 overcast hot high false P 4 rain mild high false P 5 rain cool normal false P 6 rain cool normal true N 7 overcast cool normal true P 8 sunny mild high false N 9 sunny cool normal false P 10 rain mild normal false P 11 sunny mild normal true P 12 overcast mild high true P 13 overcast hot normal false P 14 rain mild high true N
7 A simple decision tree outlook sunny overcast rain humidity P windy high normal true false N P N P
8 The induction task If the attributes are adequate, it is possible to build a correct decision tree. Many correct decision trees are possible. Correctly classify unseen objects? (it depends...) Between 2 correct decision trees, choose the simplest one.
9 ID3 Systematical approach: Generate all decision trees and choose the simplest Possible for small induction tasks only ID3 approach: Many objects, many attributes. A reasonably good decision tree is required. Use the entropy minimization principle to select the «best» attribute
10 ID3 Result: Correct decision trees are found. Training sets of 30,000 examples Examples with 50 atttributes No convergence garantee
11 ID3 How to form a DT for a set C of objects? T = test of the value of a given attribute on an object The possible values (outcomes) are: O 1, O 2,..., O w. Partition = {C 1, C 2,..., C w } of C. C i contains objects of C whose value (outcome) is O i.
12 A structuring tree of C T O 1 O 2... O w C 1 C 2... C w
13 2 assumptions: Choice of the test (1) the test set is in the proportion of the training set: p: number of positive (+) examples n: number of negative (-) examples P + : probability to be positive = p/(p+n) P - : probability to be negative = n/(p+n) (2) Information gain based on the entropy E(p, n): E(p, n) = - P + log(p + ) - P - log(p - ) (entropy disorder)
14 Choice of the test A attribute with values in {A 1, A 2,..., A w } C = {C 1, C 2,..., C w } objects in C i have A = A i. C i has p i objects in P and n i objects in N. E(p i, n i ) = entropy of of C i.
15 Entropy function A measure of disorder For x in ]0, 1[ : E(x) = -xlog(x) (1-x)log(1-x) E(0) = E(1) = 0 No disorder E is a bell function maximum for x=1/2 (maximal disorder) Vertical in 0 and 1. E(1/2) = log(2) 0.7 (... approximate values: log(3) 1.1 log(4) 1.4 log(5) 1.6 log(7) 2)
16 Entropy function p positive objects and n negative objects... What is the entropy E(p n) of the proportion (p n)? E(p n) = - p/(p+n)log(p/(p+n)) - n/(p+n)log(n/(p+n)) = log(p+n) - p/(p+n)log(p) - n/(p+n)log(n)
17 Choice of the test «Entropy a priori» (Eap) of attribute A: A measure of what could be the average entropy if we ask the value of attribute A A weighted sum of the entropies associated to each value of A The weight of value Ai is in proportion of the number of objects with value Ai Eap(A) = Σ i E(p i, n i )(p i +n i )/(p+n) Choose attribute A* = argmin b Eap(b) (i.e. looking for the attribute that minimizes disorder...)
18 Choice of the test Example, the entropy «a priori» of each attribute Eap(outlook) = 0.45 Eap(temperature) = 0.65 Eap(humidity) = 0.55 Eap(windy) = 0.65 ID3 chooses «outlook» as the DT root attribute.
19 ID3 Complexity: O ( C. A.D) C : size of the training set A : number of attributes D : depth of the decision tree
20 Noise Error in attribute values Object 1. outlook = overcast 1 and 3 identical, but belong to different classes. Misclassification: Object 3 corrupted to belong to N The DT becomes complex (12 nodes)
21 Noise Two requirements: (R1) Being able to work with inadequate attributes (R2) Being able to decide that testing further attributes will not improve the predictive accuracy of the DT.
22 Noise What to do when an attribute is inadequate or irrelevant? Create a leaf with which kind of value? Most numerous class: P or N Probability of belonging to P
23 Unknown attribute values 2 questions: How to build the DT? How to deal them during classification?
24 Unknown attribute values How to build the DT? Bayesian approach - DT approach - «most common value» approach - «unknown» as a value - - the «proportion» approach ++
25 Unknown attribute values Assume the value of A is unkown for few objects (= '?') p u number of objects in P with A unknown n u number of objects in N with A unknown Objects with unknown values are distributed across the values of in proportion the relative frequency of these values in C p i := p i + p u r i where r i = (p i +n i )/((p+n)-(p u - n u )) (number of objects with value Ai: p i +n i ) (Number of objects with A value known: (p+n)-(p u - n u ))
26 Summary Induction task = find out DT for classification 2 classes, ~1000 attributes, ~50 values Choice of root test based on information theory Minimization of entropy Noise Unknown attribute values Approximate method
27 Reference J.R. Quinlan, «Induction on decision trees», Machine Learning (1986)
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