Question 3
Given the following data points, use the K-Nearest Neighbours
(kNN) (k=5) to
find the class for age<=30, income=medium, student=yes,
credit-rating=fair.
Show your calculations and the final clusters. For similarity
measure use a simple
match of attribute values: Similarity(A,B)=
equals bi and 0 otherwise. ai and b i are either age, income,
student or credit_rating. Weights are all 1 except
for income it is 2.
Question 3 Given the following data points, use the K-Nearest Neighbours (kNN) (k=5) to find the class for age<=30, income=medium, student=yes, credit-rating=fair. Show your calculations and the fi...
Given the training set below: Age Income Student Credit_rating Buys_computer <=30 high no fair no <=30 high no excellent no 31…40 high no fair no >40 medium no fair yes <=30 low no fair yes >40 high no fair no >40 low yes fair yes >40 low yes excellent no 31…40 low yes excellent yes <=30 medium no fair no <=30 low yes fair yes >40 medium yes fair yes <=30 medium yes excellent no 31…40 medium no excellent no...
Group of answer choices 1. IF Age=31..40 AND Income=medium THEN Buys_computer=yes; 2. IF Age<=30 AND Income=low THEN Buys_computer=no; 3. IF Age<=30 AND Student=yes THEN Buys_computer=no; 4. IF Age<=30 AND Credit_rating=excellent THEN Buys_computer=yes; 5. IF Age>40 AND Credit_rating=excellent THEN Buys_computer=no; 6. IF Age=31..40 THEN Buys_computer=yes; Given the decision tree in the image, which of the following are rules extracted from it? (Select all that apply) age? <=30 31..40 >40 student? income? credit rating? no yes excellent low medium, high fair no...
Group of answer choices (Select all that apply) 1. Age: 37; Income: medium; Student: no; Credit_rating: excellent; 2. Age: 45; Income: low; Student: yes; Credit_rating: excellent; 3. Age: 32; Income: high; Student: no; Credit_rating: fair; 4. Age: 30; Income: medium; Student: no; Credit_rating: excellent; 5. Age: 40; Income: low; Student: yes; Credit_rating: fair; 6. Age: 39; Income: medium; Student: yes; Credit_rating: fair; Given the decision tree in the image, which of the following are rules extracted from it? (Select all...
Given the training data in Question 1 below| (on buying RRSP8), predict the class of the following new example using k-nearest-neighbor classification fork = 5: sector = oil industry, income = medium, self-employed = yes, credit-rating fair. For distance measure, use the following similarity measure: similarity(tupleAtupleB)-4-.(w"S(ab/4), where S(ab) is 1 if parameter a equals parameter b and o otherwise The parameters atand biare either Sector, income, self-employed, or credit-rating. The weights wiare all 1, except for income, which is 2....
Given the training data in Question 1 below| (on buying RRSP8), predict the class of the following new example using k-nearest-neighbor classification fork = 5: sector = oil industry, income = medium, self-employed = yes, credit-rating fair. For distance measure, use the following similarity measure: similarity(tupleAtupleB)-4-.(w"S(ab/4), where S(ab) is 1 if parameter a equals parameter b and o otherwise The parameters atand biare either Sector, income, self-employed, or credit-rating. The weights wiare all 1, except for income, which is 2....