You are asked to build a machine learning system to estimate someone's blood pressure (two numbers: systolic and diastolic; consider them to be real-valued) based on the following inputs: the patient's sex, age, weight, average grams of fat consumed per day, number of servings of red meat per week, servings of fruits and vegetables per day, smoker or non-smoker. You are given a training data set of values for all of these variables and the blood pressure numbers for 10,000 patients. Answer (and explain) the following questions: (a) What kind of machine learning problem is this? (b) Is it a predictive task or a descriptive task? (c) Are you likely to use a geometric model, a probabilistic model, or a logical model? (d) Will your model be a grouping model or a grading model? (e) What is the label space for this problem? (f) What is the output space for this problem?