please help answer the questions in this image

Q4 k-Nearest Neighborhood 36 Points Consider a binary classification problem with two real valued features and 82. Figures 1 and 2 illustrate two different training sets Sl and S2. White circles indicate the positively-labeled examples, whereas black squares indicate the negatively-labeled examples. To classify a new instance point, we will use (unweighted) k-Nearest Neighbors with Euclidean distance and different k values. Thus the label for a new point will be predicted by the majority class (i.e., positive or negative) among the k closest examples around the query point. o os 1 1.5 2 2s 3 3.5 4 as s ss 6 Figure 1: The training set Sl 04.1 8 Points Draw the decision boundaries of Sl and 82 when k @ Please select file(s) Select file(s) Save Answer 04.2 5 Points 0 0-5 1.5 2 as 3 3.5 4 4s 5 5s 6 Figure 2: The training set S2 Which label would you predict for the query points (3, 2) and (4, 2) given the decision boundary of Sl in 04.1? Ties must be broken toward predicting the positive class. (Auto: Your answer must be look like "negative, negative") Enter your answer here Save Answer 04.3 5 Points Which label would you predict for the query points (4.5, 4) and (4, 2) given the decision boundary of S2 in 04.1? Ties must be broken toward predicting the positive class. (Auto: Your answer must be look like "negative, negative") Enter your answer here Save Answer 10 Points When k > 1, a partition of spaces like the above is called the k-th order Voronoi Diagram or Voronoi Tesselation. Try to draw the decision boundaries of Sl when k 3. (Hint: Try to draw every bisector between all pairs of positive and negative examples) Please select file(s) Select file(s) Save Answer 04.5 8 Points If the X2-coordinate of four example points in Sl are multiplied by 5, what would happen to its decision boundary when k 1? Could this effect cause problems when working with real data? Describe your idea: how to alleviate it. (Free Response) Enter your answer here Save Answer