Question

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)=

5%, *c(a,,b,)/4 ,h ) is 1 ifai where cla -1
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.

studentcredit rating dass RID age income high high medium no fair excellent fair fair fair excellent excellent fair fair <=30

5%, *c(a,,b,)/4 ,h ) is 1 ifai where cla -1
studentcredit rating dass RID age income high high medium no fair excellent fair fair fair excellent excellent fair fair
0 0
Add a comment Improve this question Transcribed image text
Answer #1

k- Nearest nethbr alyed nackine learning, wtorba, Each elets clasnged by a Nhayony berg as ned ho 1E class most common measunas age, inco収ノ -SlGdent, Credit.nl a他 (sM) nea svchetueen _c.wlav tg Col awe· Now ue,O, 구5 class RID Yes No Yco lo Its ItClass yo

Add a comment
Know the answer?
Add Answer to:
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...
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for? Ask your own homework help question. Our experts will answer your question WITHIN MINUTES for Free.
Similar Homework Help Questions
  • Given the training set below: Age Income Student Credit_rating Buys_computer <=30 high no fair no <=30...

    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...

    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:...

    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...

    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...

    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....

ADVERTISEMENT
Free Homework Help App
Download From Google Play
Scan Your Homework
to Get Instant Free Answers
Need Online Homework Help?
Ask a Question
Get Answers For Free
Most questions answered within 3 hours.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT