Learning from Observations         Chapter 18        Section 1 – 3
Outline• Learning agents• Inductive learning• Decision tree learning
Learning• Learning is essential for unknown environments,  – i.e., when designer lacks omniscience• Learning is useful as ...
Learning agents
Learning element• Design of a learning element is affected by  – Which components of the performance element are to    be ...
Inductive learning• Simplest form: learn a function from examplesf is the target functionAn example is a pair (x, f(x))Pro...
Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all...
Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all...
Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all...
Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all...
Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all...
Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all...
Learning decision treesProblem: decide whether to wait for a table at a restaurant,  based on the following attributes:   ...
Attribute-based representations•   Examples described by attribute values (Boolean, discrete, continuous)•   E.g., situati...
Decision trees• One possible representation for hypotheses• E.g., here is the “true” tree for deciding whether to wait:
Expressiveness•   Decision trees can express any function of the input attributes.•   E.g., for Boolean functions, truth t...
Hypothesis spacesHow many distinct decision trees with n Boolean attributes?= number of Boolean functions                 ...
Hypothesis spacesHow many distinct decision trees with n Boolean attributes?= number of Boolean functions                 ...
Decision tree learning•   Aim: find a small tree consistent with the training examples•   Idea: (recursively) choose "most...
Choosing an attribute• Idea: a good attribute splits the examples into subsets  that are (ideally) "all positive" or "all ...
Using information theory• To implement Choose-Attribute in the DTL  algorithm• Information Content (Entropy):       I(P(v1...
Information gain• A chosen attribute A divides the training set E into  subsets E1, … , Ev according to their values for A...
Information gainFor the training set, p = n = 6, I(6/12, 6/12) = 1 bitConsider the attributes Patrons and Type (and others...
Example contd.• Decision tree learned from the 12 examples:• Substantially simpler than “true” tree---a more complex  hypo...
Performance measurement•    How do we know that h ≈ f ?    1.       Use theorems of computational/statistical learning the...
Summary• Learning needed for unknown environments,  lazy designers• Learning agent = performance element +  learning eleme...
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M18 learning

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M18 learning

  1. 1. Learning from Observations Chapter 18 Section 1 – 3
  2. 2. Outline• Learning agents• Inductive learning• Decision tree learning
  3. 3. Learning• Learning is essential for unknown environments, – i.e., when designer lacks omniscience• Learning is useful as a system construction method, – i.e., expose the agent to reality rather than trying to write it down• Learning modifies the agents decision mechanisms to improve performance
  4. 4. Learning agents
  5. 5. Learning element• Design of a learning element is affected by – Which components of the performance element are to be learned – What feedback is available to learn these components – What representation is used for the components• Type of feedback: – Supervised learning: correct answers for each example – Unsupervised learning: correct answers not given – Reinforcement learning: occasional rewards
  6. 6. Inductive learning• Simplest form: learn a function from examplesf is the target functionAn example is a pair (x, f(x))Problem: find a hypothesis h such that h ≈ f given a training set of examples(This is a highly simplified model of real learning: – Ignores prior knowledge – Assumes examples are given) –
  7. 7. Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all examples)• E.g., curve fitting:••
  8. 8. Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all examples)• E.g., curve fitting:••
  9. 9. Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all examples)• E.g., curve fitting:••
  10. 10. Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all examples)• E.g., curve fitting:••
  11. 11. Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all examples)• E.g., curve fitting:•
  12. 12. Inductive learning method• Construct/adjust h to agree with f on training set• (h is consistent if it agrees with f on all examples)• E.g., curve fitting:• Ockham’s razor: prefer the simplest hypothesis consistent with data
  13. 13. Learning decision treesProblem: 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)
  14. 14. Attribute-based representations• Examples described by attribute values (Boolean, discrete, continuous)• E.g., situations where I will/wont wait for a table:• Classification of examples is positive (T) or negative (F)•
  15. 15. Decision trees• One possible representation for hypotheses• E.g., here is the “true” tree for deciding whether to wait:
  16. 16. Expressiveness• Decision trees can express any function of the input attributes.• E.g., for Boolean functions, truth table row → path to leaf:• Trivially, there is a consistent decision tree for any training set with one path to leaf for each example (unless f nondeterministic in x) but it probably wont generalize to new examples• Prefer to find more compact decision trees
  17. 17. Hypothesis spacesHow many distinct decision trees with n Boolean attributes?= number of Boolean functions n= number of distinct truth tables with 2n rows = 22• E.g., with 6 Boolean attributes, there are 18,446,744,073,709,551,616 trees
  18. 18. Hypothesis spacesHow many distinct decision trees with n Boolean attributes?= number of Boolean functions n= number of distinct truth tables with 2n rows = 22• E.g., with 6 Boolean attributes, there are 18,446,744,073,709,551,616 treesHow many purely conjunctive hypotheses (e.g., Hungry ∧ ¬Rain)?• Each attribute can be in (positive), in (negative), or out ⇒ 3n 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
  19. 19. Decision tree learning• Aim: find a small tree consistent with the training examples• Idea: (recursively) choose "most significant" attribute as root of (sub)tree
  20. 20. 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•
  21. 21. Using information theory• To implement Choose-Attribute in the DTL algorithm• Information Content (Entropy): I(P(v1), … , P(vn)) = Σi=1 -P(vi) log2 P(vi)• For a training set containing p positive examples and n negative examples: p n p p n n I( , )=− log 2 − log 2 p+n p+n p+n p+n p+n p+n
  22. 22. Information gain• A chosen attribute A divides the training set E into subsets E1, … , Ev according to their values for A, where A has v distinct values. v p i + ni pi ni remainder ( A) = ∑ I( , ) i =1 p + n pi + ni pi + ni• 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
  23. 23. Information gainFor the training set, p = n = 6, I(6/12, 6/12) = 1 bitConsider 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 4Patrons has the highest IG of all attributes and so is chosen by the DTL algorithm as the root
  24. 24. Example contd.• Decision tree learned from the 12 examples:• Substantially simpler than “true” tree---a more complex hypothesis isn’t justified by small amount of data
  25. 25. 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 (
  26. 26. 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

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