Question 10 Suppose we are using a Perceptron algorithm to predict if a point lies above or below the line y=2x-3. The next point in the test set is (1,2). The algorithm predicts that the point lies...

Extracted text: Question 10 Suppose we are using a Perceptron algorithm to predict if a point lies above or below the line y=2x-3. The next point in the test set is (1,2). The algorithm predicts that the point lies below the line. Question 9 Assuming the learning rate is 0.01 and the current value for the weights are: weight for x: 0.5 weight for y: 0.025 Suppose we are using a Perceptron algorithm to predict if a point lies above or below the line y=2x-3. The first point in the test set is (0,-2). The algorithm What is the new weight for x? predicts that the point lies below the line. O 5.02 What happens next? O 4.98 O The weights are NOT changed because the algorithm predicted correctly. O 5.2 O The weights are changed because the algorithm predicted incorrectly. O 4.8 Question 11 Question 12 Consider the logistic function Assume we are using Logistic Regression to predict the binary classification problem of whether a student should be admitted into a college based solely 1+e et upon their SAT score. We assume that 1 implies true and O false. The logistic regression algorithm predicts m- 2.2 and b - 1240 for the logistic function Which of the following is true about the graph of the function? the y values range between -1 and 1 What does the algorithm predict for a student with SAT 1220? the plot is centered at x»2 O No answer text provided. O the plot is centered at x-4 O admit O the plot is centered at x-8 O don't admit Question 13 Assume we are using Logistic Regression to predict the binary classification problem of whether a student should be admitted into a college based solely upon their SAT score. We assume that 1 implies true and O false. The logistic regression algorithm predicts the logistic function What does the algorithm predict for a student with SAT 1220? don't admit O admit Question 14 Question 15 Which of the following is NOT true about the k Nearest Neighbor algorithm? Using the same training and testing sets, different classification algorithms may give different results. O the number of neighbors, k, to use must be input by the user O depending on the value of k and the data, the resulting classification may not be unique O True O the algorithm is iterative O False