1. Machine Learning: finding patterns

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 ?

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

11. The weather problem Given past data, Can you come up with the rules for Play/Not Play ? What is the game? 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

14. 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

15. 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

16. A decision tree for this problem witten&eibe

17. 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 ...

19. 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

26. *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

32. Weka