(70) 2- An observation dataset which has 7 instances has been collected like the following one....

 

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(70) 2- An observation dataset which has 7 instances has been collected like the following one. For the target attribute "stolen", the target of a new instance as "5 - Red - Sports - Imported" is wanted to be predicted. (35) a) Find the target by using Naive Bayes (35) b) Find the target by using 3-NN id 1 2 3 4 5 6 7 type Sports Sports Sports Sports SUV SUV SUV Sports Hint: When it is necessary, use fixed 2-interval discretization, 0-1 normalization, and Manhattan distance. Formulas: Min-max normalization v²= year 1 8 2 9 10 4 12 5 v-min max-min color Red Manhattan Distance → Σ=o \X; - Y;| Red Red Yellow Yellow Yellow Red Red x (new_max-new_min) + new_min Fix k-interval discretization : Vmin is the minimum observed value, Vmax is the maximum observed value Intervals have width w = (Vmax - Vmin)/k The cut points are min+W, Vmin + 2w, ... , Vmin + (k-1)w origin Domestic Domestic Domestic Domestic Imported Imported Imported Imported Naive Bayes: Likelihood stolen Yes Posterior Probability No Yes No No Yes No ? Class Prior Probability P(c|x)=P(x\c)P(c) P(x). Predictor Prior Probability P(c|X) = P(x₁ | c) x P(x₂ | c)xx P(x,c)x P(c) (70) 2- An observation dataset which has 7 instances has been collected like the following one. For the target attribute "stolen", the target of a new instance as "5 - Red - Sports - Imported" is wanted to be predicted. (35) a) Find the target by using Naive Bayes (35) b) Find the target by using 3-NN id 1 2 3 4 5 6 7 type Sports Sports Sports Sports SUV SUV SUV Sports Hint: When it is necessary, use fixed 2-interval discretization, 0-1 normalization, and Manhattan distance. Formulas: Min-max normalization v²= year 1 8 2 9 10 4 12 5 v-min max-min color Red Manhattan Distance → Σ=o \X; - Y;| Red Red Yellow Yellow Yellow Red Red x (new_max-new_min) + new_min Fix k-interval discretization : Vmin is the minimum observed value, Vmax is the maximum observed value Intervals have width w = (Vmax - Vmin)/k The cut points are min+W, Vmin + 2w, ... , Vmin + (k-1)w origin Domestic Domestic Domestic Domestic Imported Imported Imported Imported Naive Bayes: Likelihood stolen Yes Posterior Probability No Yes No No Yes No ? Class Prior Probability P(c|x)=P(x\c)P(c) P(x). Predictor Prior Probability P(c|X) = P(x₁ | c) x P(x₂ | c)xx P(x,c)x P(c)

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A data set is a set of data that is collected for a specific purpose There are many ways in which data can be collectedfor example as part of service delivery oneoff surveys interviews observations an... View the full answer