[SOLVED] Machine Learning Homework 2

0. 0. I. Pen-and-paper [13v]
      Four positive observations, {(𝐴) , (𝐵) , (𝐴) , (𝐴)}, and four negative observations, {(𝐵) , (𝐵) , (𝐴) , (𝐵)},

      were collected. Consider the problem of classifying observations as positive or negative.

1) [4v] Compute the recall of a distance-weighted 𝑘NN with 𝑘 = 5 and distance 𝑑(𝐱1, 𝐱2) =

𝐻𝑎𝑚𝑚𝑖𝑛𝑔(𝐱1, 𝐱2) + 1 using leave-one-out evaluation schema (i.e., when classifying one 2

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observation, use all remaining ones).
An additional positive observation was acquired, (𝐵), and a third variable 𝑦3 was independently

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monitored, yielding estimates 𝑦3|𝑃 = {1.2, 0.8, 0.5, 0.9,0.8} and 𝑦3|𝑁 = {1, 0.9, 1.2, 0.8}.

2) [4v] Considering the nine training observations, learn a Bayesian classifier assuming:
i) 𝑦1 and 𝑦2 are dependent, ii) {𝑦1, 𝑦2} and {𝑦3} variable sets are independent and equally important, and ii) 𝑦3 is normally distributed. Show all parameters.

𝐴𝐵𝐵
Considering three testing observations, {(( 1 ) , Positive) , ((1) , Positive) , (( 0 ) , Negative)}.

0.8 1 0.9

3. 3)  [3v] Under a MAP assumption, compute 𝑃(Positive|𝐱) of each testing observation.
4. 4)  [2v] Given a binary class variable, the default decision threshold of 𝜃 = 0.5, 𝑓(𝐱|𝜃) = { Positive 𝑃(Positive|𝐱) > 𝜃

   Negative otherwise

   can be adjusted. Which decision threshold – 0.3, 0.5 or 0.7 – optimizes testing accuracy? II. Programming and critical analysis [7v]

Considering the pd_speech.arff dataset available at the course webpage.

5. 5)  [3v] Using sklearn, considering a 10-fold stratified cross validation (random=0), plot the cumulative testing confusion matrices of 𝑘NN (uniform weights, 𝑘 = 5, Euclidean distance) and Naïve Bayes (Gaussian assumption). Use all remaining classifier parameters as default.
6. 6)  [2v] Using scipy, test the hypothesis “𝑘NN is statistically superior to Naïve Bayes regarding accuracy”, asserting whether is true.
7. 7)  [2v] Enumerate three possible reasons that could underlie the observed differences in predictive accuracy between 𝑘NN and Naïve Bayes.

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