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BIT405 Business Intelligence

tag 0 Download 2 Pages / 404 Words tag 26-08-2021

Questions:

Part A: 

K-Nearest Neighbor (KNN) is a supervised learning algorithm where the result of new instance query is classified based on majority of K-nearest neighbor category. The purpose of this algorithm is to classify a new object based on attributes and training samples. Indeed, KNN used neighborhood classification as the prediction value of the new query instance.

The following data classifying the Power Saving Lights by their economical feasibility as Preserver or Wasteful

We consider 2 factors for classifying:

X1: Lightning Duration

X2: Power Consuming

We suppose use the number of nearest neighbor’s k = 2.

The following data presents six training samples, using the KNN algorithm, classify the last sample as Preserver or Wasteful assuming that X1 = 10 and X2 = 500

X1: Lightning Duration (Hours)

X2: Power Consuming (Watts)

Y: Classification

6

900

Wasteful

2

150

Wasteful

5

600

Wasteful

3

80

Preserver

4

200

Wasteful

2

60

Preserver

10

500

???????

Table 1: Training data

1). Calculate the Euclidian distance between the query-instance and all the training samples. Insert values in table 2 and provide detail of calculus.

X1: Lightning Duration (Hours)

X2: Power Consuming (Watts)

Euclidian distance to the query-instance (10, 500)

6

900

 

2

150

 

5

600

 

3

80

 

4

200

 

2

60

 

Table 2: Euclidian distance between the query-instance and all the training samples

2). Sort the distance and determine nearest neighbors based on the k-th minimum distance. Insert values in table 3.

X1: Lightning Duration (Hours)

X2: Power Consuming (Watts)

Euclidian distance to the query-instance (10, 500)

Rank minimum distance

Is it included in 2-nearest neighbors?

6

900

 

 

 

2

150

 

 

 

5

600

 

 

 

3

80

 

 

 

4

200

 

 

 

2

60

 

 

 

Table 3: Section of the 2-nearest neighbors

3) Gather the category Y of the nearest neighbors. Insert values in table 4 and justify your response.          

X1: Lightning Duration (Hours)

X2: Power Consuming (Watts)

Euclidian distance to the query-instance (10, 500)

Rank minimum distance

Is it included in 2-nearest neighbors?

Y= category of nearest neighbor

6

900

 

 

 

 

2

150

 

 

 

 

5

600

 

 

 

 

3

80

 

 

 

 

4

200

 

 

 

 

2

60

 

 

 

 

 Table 4: Categories of the 2-nearest neighbors

4) Use simple majority of the category of nearest neighbors as the prediction value of the query instance.

Part B:

Let us consider the training data below dealing with “Eye disease problem” to learn Naive Bayes Classifier.

The goal is to classify (as “Noncontact”, as “Soft Contact or as “Hard contact”) a new record: R11: (Pre-presbyopic, Hypermetrope,Yes, Reduced)

For this purpose you have to calculate P(NonContact), P(Hard Contact), and P(Soft Contact)

  1. Compute the conditional probabilities and class priors for each class label in the training set. (3.75 marks, 0.25 for each value)
  1. Compute the probability to assign each class label for the new record. (0.75 marks,0.25 for each value)

Class Label = Soft Contact:

Class Label = Hard Contact:

  1. Which class is to assign to the new record ? justify your answer
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Cite This Work

To export a reference to this article please select a referencing stye below:

My Assignment Help (2021) Business Intelligence [Online]. Available from: https://myassignmenthelp.com/free-samples/bit405-business-intelligence/calculate-the-euclidian-distance.html
[Accessed 27 September 2022].

My Assignment Help. 'Business Intelligence' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/bit405-business-intelligence/calculate-the-euclidian-distance.html> accessed 27 September 2022.

My Assignment Help. Business Intelligence [Internet]. My Assignment Help. 2021 [cited 27 September 2022]. Available from: https://myassignmenthelp.com/free-samples/bit405-business-intelligence/calculate-the-euclidian-distance.html.


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