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Chapter18 Learning from Observations Sec. 1 - 3 20070607 - PDF document

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Chapter18 Learning from Observations Sec. 1 - 3 20070607 Chap18 1 Learning Essential for unknown environments. i.e. when designer lacks omniscience Learning modifies the agents decision mechanisms to improve performance

  1. Chapter18 Learning from Observations Sec. 1 - 3 20070607 Chap18 1 Learning Essential for unknown environments. • i.e. when designer lacks omniscience Learning modifies the agent’s decision • mechanisms to improve performance 20070607 Chap18 2 1

  2. Learning Agents Performance Element • Decides what actions to take - Learning Element • Modifies the performance element to make better decision - 20070607 Chap18 3 Inductive Learning Given as input the correct value of the unknown • function for particular inputs and must try to recover the unknown function or something close it. Pure inductive Inference (or Induction) • Given a collection of examples of f , return a function h that approximates f . An example is a pair: (x, f(x)) h: hypothesis function, f: target function It is not easy to tell whether any particular h is a • good approximation of f . 20070607 Chap18 4 2

  3. Inductive Learning Method Construct/adjust h to agree with f on • training set) ( h is consistent if it agrees with f on all examples) 20070607 Chap18 5 Inductive Learning Method (cont.-1) 20070607 Chap18 6 3

  4. Inductive Learning Method (cont.-2) 20070607 Chap18 7 Inductive Learning Method (cont.-3) 20070607 Chap18 8 4

  5. Inductive Learning Method (cont.-4) 20070607 Chap18 9 Inductive Learning Method (cont.-5) How do we choose from among multiple • consistent hypotheses? Ockham’s razor Prefer the simplest hypothesis consistent with the data 20070607 Chap18 10 5

  6. Attribute-based Representations Examples described by attribute values (Boolean, discrete, continuous, etc.) e.g. situations where I will/won’t wait for a table 20070607 Chap18 11 Learning Decision Trees Problem: decide whether to wait for a table at a restaurant, based on the following attributes: 1. Alternate: is there an alternative restaurant nearby? 2. Bar: is there a comfortable bar area to wait in? 3. Fri/Sat: is today Friday or Saturday? 4. Hungry: are we hungry? 5. Patrons: number of people in the restaurant (None, Some, Full) 6. Price: price range ($, $$, $$$) 7. Raining: is it raining outside? 8. Reservation: have we made a reservation? 9. Type: kind of restaurant (French, Italian, Thai, Burger) 10. WaitEstimate: estimated waiting time (0-10, 10-30, 30-60, >60) 20070607 Chap18 12 6

  7. Decision Trees 20070607 Chap18 13 Expressiveness Decision trees can express any function of the • input attributes. Trivially, exist a consistent decision tree for any • training set. Prefer to find more compact decision trees. • 20070607 Chap18 14 7

  8. Hypothesis Spaces How many distinct decision trees with n Boolean attributes? = number of Boolean functions = number of distinct truth tables with 2 n rows n 2 2 = e.g. with 6 Boolean attributes, there are 18,446,744,073,709,551,616 trees. 20070607 Chap18 15 Hypothesis Spaces (cont.) How many purely conjunctive hypotheses ? (e.g., Hungry ∧ ¬ Rain) Each attribute can be in (positive), in (negative), or out • ⇒ 3 n distinct conjunctive hypotheses More expressive hypothesis space • - increases chance that target function can be expressed - increases number of hypotheses consistent with training set ⇒ may get worse predictions 20070607 Chap18 16 8

  9. Decision Tree Learning Aim: find a small tree consistent with the training example Idea: (recursively) choose “most significant” attribute as root of (sub)tree 20070607 Chap18 17 Choosing An Attribute Idea: a good attribute splits the examples into subsets that are (ideally) “all positive” or “all negative” Patrons? is a better choice. 20070607 Chap18 18 9

  10. Using Information Theory To implement Choose-Attribute in the DTL algorithm • Information Content (Entropy): • n ∑ = − I ( P ( v ),...., P ( v )) P ( v ) log P ( v ) 1 n i 2 i = i 1 For a training set containing p positive examples • and n negative examples: p n p p n n = − − I ( , ) log log + + + + + + 2 2 p n p n p n p n p n p n 20070607 Chap18 19 Information Gain A chosen attribute A divides the training set E into subsets • E 1 , … , E v according to their values for A , where A has v distinct values. + v ∑ p n p n = i i i i remainder ( A ) I ( , ) + + + p n p n p n = i 1 i i i i Information Gain (IG) or reduction in entropy from the • attribute test: p n = − IG ( A ) I ( , ) remainder ( A ) + + p n p n Choose the attribute with the largest IG • 20070607 Chap18 20 10

  11. Information Gain (cont.) For the training set, p = n = 6, • I(6/12, 6/12) = 1 bit Consider the attributes Patrons and Type • (and others too): 2 4 6 2 4 = − + + = IG ( Patrons ) 1 [ I ( 0 , 1 ) I ( 1 , 0 ) I ( , )] . 0541 bits 12 12 12 6 6 2 1 1 2 1 1 4 2 2 4 2 2 = − + + + = IG ( Type ) 1 [ I ( , ) I ( , ) I ( , ) I ( , )] 0 bits 12 2 2 12 2 2 12 4 4 12 4 4 Patrons has the highest IG of all attributes and so • is chosen by the DTL algorithm as the root 20070607 Chap18 21 Example Decision tree learned from the 12 examples: • Substantially simpler than “true” tree---a more • complex hypothesis isn’t justified by small amount of data 20070607 Chap18 22 11

  12. Performance Measurement How do we know that h ≈ f ? • 1. Use theorems of computational/statistical learning theory 2. Try h on a new test set of examples (use same distribution over example space as training set) Learning curve = % correct on test set as a function of training set size 20070607 Chap18 23 Summary Learning needed for unknown environments, • lazy designers Learning agent • = performance element + learning element For supervised learning, the aim is • to find a simple hypothesis approximately consistent with training examples Decision tree learning using information gain • Learning performance = prediction accuracy • measured on test set 20070607 Chap18 24 12

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