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Machine Learning: finding patterns
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Finding patterns ,[object Object],[object Object],[object Object],[object Object],[object Object]
Machine learning techniques ,[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Can machines really learn? ,[object Object],To get knowledge of by study, experience, or being taught To become aware by information or from observation To commit to memory To be informed of, ascertain; to receive instruction witten&eibe Difficult to measure Trivial for computers Things learn when they change their behavior in a way that makes them perform better in the future. ,[object Object],Does a slipper learn? ,[object Object]
Classification Learn a method for predicting the instance class from pre-labeled (classified)  instances Many approaches: Regression,  Decision Trees, Bayesian, Neural Networks,  ...  Given a set of points from classes  what is the class of new point  ?
Classification: Linear Regression ,[object Object],[object Object],[object Object],[object Object]
Classification: Decision Trees X Y if X > 5 then blue else if Y > 3 then blue else if X > 2 then green else blue 5 2 3
Classification: Neural Nets ,[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
The weather problem Given past data, Can you come up with the rules for  Play/Not Play ? What is the game? no true high mild rainy yes false normal hot overcast yes true high mild overcast yes true normal mild sunny yes false normal mild rainy yes false normal mild sunny no false high mild sunny yes true normal mild overcast no true normal mild rainy yes false normal mild rainy yes false high mild rainy yes false high hot overcast no true high hot sunny no false high hot sunny Play Windy Humidity Temperature Outlook
The   weather problem ,[object Object],… … … … … Yes False Normal Mild Rainy Yes False High Hot  Overcast  No True High  Hot  Sunny No False High Hot Sunny Play Windy Humidity Temperature Outlook
The   weather problem ,[object Object],witten&eibe … … … … … Yes False Normal Mild Rainy Yes False High Hot  Overcast  No True High  Hot  Sunny No False High Hot Sunny Play Windy Humidity Temperature Outlook If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity = normal then play = yes If none of the above then play = yes
Weather data with mixed attributes no true 91 71 rainy yes false 75 81 overcast yes true 90 72 overcast yes true 70 75 sunny yes false 80 75 rainy yes false 70 69 sunny no false 95 72 sunny yes true 65 64 overcast no true 70 65 rainy yes false 80 68 rainy yes false 96 70 rainy yes false 86 83 overcast no true 90 80 sunny no false 85 85 sunny Play Windy Humidity Temperature Outlook
Weather data with mixed attributes ,[object Object],… … … … … Yes False 80 75 Rainy Yes False 86 83 Overcast  No True 90 80 Sunny No False 85 85 Sunny Play Windy Humidity Temperature Outlook
Weather data with mixed attributes ,[object Object],witten&eibe … … … … … Yes False 80 75 Rainy Yes False 86 83 Overcast  No True 90 80 Sunny No False 85 85 Sunny Play Windy Humidity Temperature Outlook If outlook = sunny and humidity > 83 then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity < 85 then play = yes If none of the above then play = yes
The contact lenses data witten&eibe None Reduced Yes Hypermetrope Pre-presbyopic  None Normal Yes Hypermetrope Pre-presbyopic None Reduced No Myope Presbyopic None Normal No Myope Presbyopic None Reduced Yes Myope Presbyopic Hard Normal Yes Myope Presbyopic None Reduced No Hypermetrope Presbyopic Soft Normal No Hypermetrope Presbyopic None Reduced Yes Hypermetrope Presbyopic None Normal Yes Hypermetrope Presbyopic Soft Normal No Hypermetrope Pre-presbyopic None Reduced No Hypermetrope Pre-presbyopic Hard Normal Yes Myope Pre-presbyopic None Reduced Yes Myope Pre-presbyopic Soft Normal No Myope Pre-presbyopic None Reduced No Myope Pre-presbyopic hard Normal Yes Hypermetrope Young None Reduced Yes Hypermetrope Young Soft Normal No Hypermetrope Young None Reduced No Hypermetrope Young Hard Normal Yes Myope Young None Reduced Yes Myope Young  Soft Normal No Myope Young None Reduced No Myope Young Recommended lenses Tear production rate Astigmatism Spectacle prescription Age
A complete and correct rule set witten&eibe If tear production rate = reduced then recommendation = none If age = young and astigmatic = no and tear production rate = normal then recommendation = soft If age = pre-presbyopic and astigmatic = no and tear production rate = normal then recommendation = soft If age = presbyopic and spectacle prescription = myope and astigmatic = no  then recommendation = none If spectacle prescription = hypermetrope and astigmatic = no and tear production rate = normal then recommendation = soft If spectacle prescription = myope and astigmatic = yes and tear production rate = normal then recommendation = hard If age young and astigmatic = yes  and tear production rate = normal then recommendation = hard If age = pre-presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none If age = presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none
A decision tree for this problem witten&eibe
Classifying iris flowers witten&eibe … … … Iris virginica 1.9 5.1 2.7 5.8 102 101 52 51 2 1 Iris virginica 2.5 6.0 3.3 6.3 Iris versicolor 1.5 4.5 3.2 6.4 Iris versicolor 1.4 4.7 3.2 7.0 Iris setosa 0.2 1.4 3.0 4.9 Iris setosa 0.2 1.4 3.5 5.1 Type Petal width Petal length Sepal width Sepal length If petal length < 2.45 then Iris setosa If sepal width < 2.10 then Iris versicolor ...
[object Object],[object Object],Predicting CPU performance witten&eibe 0 0 32 128 CHMAX 0 0 8 16 CHMIN Channels Performance Cache (Kb) Main memory (Kb) Cycle time (ns) 45 0 4000 1000 480 209 67 32 8000 512 480 208 … 269 32 32000 8000 29 2 198 256 6000 256 125 1 PRP CACH MMAX MMIN MYCT PRP = -55.9 + 0.0489 MYCT + 0.0153 MMIN + 0.0056 MMAX + 0.6410 CACH - 0.2700 CHMIN + 1.480 CHMAX
Soybean classification witten&eibe Diaporthe stem canker 19 Diagnosis Normal 3 Condition Roots … Yes 2 Stem lodging Abnormal 2 Condition Stem … ? 3 Leaf spot size Abnormal 2 Condition Leaves ? 5 Fruit spots Normal 4 Condition of fruit pods Fruit … Absent 2 Mold growth Normal 2 Condition Seed … Above normal 3 Precipitation July 7 Time of occurrence Environment Sample value Number of values Attribute
The role of domain knowledge ,[object Object],witten&eibe If leaf condition is normal and stem condition is abnormal and stem cankers is below soil line and canker lesion color is brown then diagnosis is rhizoctonia root rot If leaf malformation is absent and stem condition is abnormal and stem cankers is below soil line and canker lesion color is brown then diagnosis is rhizoctonia root rot
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Learning as search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Enumerating the concept space ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
The version space ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
*Version space example, 1 ,[object Object],witten&eibe Start with: L ={} G ={<*, *>} First example: <green,cow>: positive How does this change L and G?
*Version space example, 2 ,[object Object],witten&eibe Result: L ={<green, cow>} G ={<*, *>} Second example: <red,chicken>: negative
*Version space example, 3 ,[object Object],witten&eibe Result: L ={<green, cow>} G ={<green,*>,<*,cow>} Final example: <green, chicken>: positive
*Version space example, 4 ,[object Object],witten&eibe Resultant version space: L ={<green, *>} G ={<green, *>}
*Version space example, 5 ,[object Object],witten&eibe L ={} G ={<*, *>} <green,cow>: positive L ={<green, cow>} G ={<*, *>} <red,chicken>: negative L ={<green, cow>} G ={<green,*>,<*,cow>} <green, chicken>: positive L ={<green, *>} G ={<green, *>}
*Candidate-elimination algorithm witten&eibe Initialize  L  and  G For each example  e: If  e  is positive: Delete all elements from  G  that do not cover  e For each element  r  in  L  that does not cover  e: Replace  r  by all of its most specific generalizations that 1. cover  e  and 2. are more specific than some element in  G Remove elements from  L  that are more general than some other element in  L If  e  is   negative: Delete all elements from  L  that cover  e For each element  r  in  G  that covers  e: Replace  r  by all of its most general specializations  that 1. do not cover  e  and  2. are more general than some element in  L  Remove elements from  G  that are more specific than some other element in  G
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Language bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Search bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Overfitting-avoidance bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
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Machine Learning: finding patterns Outline

  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Classification Learn a method for predicting the instance class from pre-labeled (classified) instances Many approaches: Regression, Decision Trees, Bayesian, Neural Networks, ... Given a set of points from classes what is the class of new point ?
  • 7.
  • 8. Classification: Decision Trees X Y if X > 5 then blue else if Y > 3 then blue else if X > 2 then green else blue 5 2 3
  • 9.
  • 10.
  • 11. The weather problem Given past data, Can you come up with the rules for Play/Not Play ? What is the game? no true high mild rainy yes false normal hot overcast yes true high mild overcast yes true normal mild sunny yes false normal mild rainy yes false normal mild sunny no false high mild sunny yes true normal mild overcast no true normal mild rainy yes false normal mild rainy yes false high mild rainy yes false high hot overcast no true high hot sunny no false high hot sunny Play Windy Humidity Temperature Outlook
  • 12.
  • 13.
  • 14. Weather data with mixed attributes no true 91 71 rainy yes false 75 81 overcast yes true 90 72 overcast yes true 70 75 sunny yes false 80 75 rainy yes false 70 69 sunny no false 95 72 sunny yes true 65 64 overcast no true 70 65 rainy yes false 80 68 rainy yes false 96 70 rainy yes false 86 83 overcast no true 90 80 sunny no false 85 85 sunny Play Windy Humidity Temperature Outlook
  • 15.
  • 16.
  • 17. The contact lenses data witten&eibe None Reduced Yes Hypermetrope Pre-presbyopic None Normal Yes Hypermetrope Pre-presbyopic None Reduced No Myope Presbyopic None Normal No Myope Presbyopic None Reduced Yes Myope Presbyopic Hard Normal Yes Myope Presbyopic None Reduced No Hypermetrope Presbyopic Soft Normal No Hypermetrope Presbyopic None Reduced Yes Hypermetrope Presbyopic None Normal Yes Hypermetrope Presbyopic Soft Normal No Hypermetrope Pre-presbyopic None Reduced No Hypermetrope Pre-presbyopic Hard Normal Yes Myope Pre-presbyopic None Reduced Yes Myope Pre-presbyopic Soft Normal No Myope Pre-presbyopic None Reduced No Myope Pre-presbyopic hard Normal Yes Hypermetrope Young None Reduced Yes Hypermetrope Young Soft Normal No Hypermetrope Young None Reduced No Hypermetrope Young Hard Normal Yes Myope Young None Reduced Yes Myope Young Soft Normal No Myope Young None Reduced No Myope Young Recommended lenses Tear production rate Astigmatism Spectacle prescription Age
  • 18. A complete and correct rule set witten&eibe If tear production rate = reduced then recommendation = none If age = young and astigmatic = no and tear production rate = normal then recommendation = soft If age = pre-presbyopic and astigmatic = no and tear production rate = normal then recommendation = soft If age = presbyopic and spectacle prescription = myope and astigmatic = no then recommendation = none If spectacle prescription = hypermetrope and astigmatic = no and tear production rate = normal then recommendation = soft If spectacle prescription = myope and astigmatic = yes and tear production rate = normal then recommendation = hard If age young and astigmatic = yes and tear production rate = normal then recommendation = hard If age = pre-presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none If age = presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none
  • 19. A decision tree for this problem witten&eibe
  • 20. Classifying iris flowers witten&eibe … … … Iris virginica 1.9 5.1 2.7 5.8 102 101 52 51 2 1 Iris virginica 2.5 6.0 3.3 6.3 Iris versicolor 1.5 4.5 3.2 6.4 Iris versicolor 1.4 4.7 3.2 7.0 Iris setosa 0.2 1.4 3.0 4.9 Iris setosa 0.2 1.4 3.5 5.1 Type Petal width Petal length Sepal width Sepal length If petal length < 2.45 then Iris setosa If sepal width < 2.10 then Iris versicolor ...
  • 21.
  • 22. Soybean classification witten&eibe Diaporthe stem canker 19 Diagnosis Normal 3 Condition Roots … Yes 2 Stem lodging Abnormal 2 Condition Stem … ? 3 Leaf spot size Abnormal 2 Condition Leaves ? 5 Fruit spots Normal 4 Condition of fruit pods Fruit … Absent 2 Mold growth Normal 2 Condition Seed … Above normal 3 Precipitation July 7 Time of occurrence Environment Sample value Number of values Attribute
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33. *Candidate-elimination algorithm witten&eibe Initialize L and G For each example e: If e is positive: Delete all elements from G that do not cover e For each element r in L that does not cover e: Replace r by all of its most specific generalizations that 1. cover e and 2. are more specific than some element in G Remove elements from L that are more general than some other element in L If e is negative: Delete all elements from L that cover e For each element r in G that covers e: Replace r by all of its most general specializations that 1. do not cover e and 2. are more general than some element in L Remove elements from G that are more specific than some other element in G
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39. Weka