Please help with the following five questions in full sentences:

Responses should be a few sentences per question/part

Explain whether each scenario is a classication or regression problem. Provide n (the number of samples) and d (the number of columns). (a) We collect a set of data on the top 500 rms in the US. For each rm we record prot, number of employees, industry and the CEO salary. We are interested in understanding which factors affect CEO salary. (b) We are considering launching a new product and wish to know whether it will be a success or a failure. We collect data on 20 similar products that were previously launched. For each product we have recorded whether it was a success or failure, price charged for the product, marketing budget, competition price, and ten other variables. (c) We are interesting in predicting the % change in the US dollar in relation to the weekly changes in the world stock markets. Hence we collect weekly data for all of 2012. For each week we record the % change in the dollar, the % change in the US market, the % change in the British market, and the % change in the German market. The table below provides a training data set containing six observa- tions, three predictors, and one qualitative response variable. Red Red Red Green Green Suppose we wish to use this data set to make a prediction for Y when X1 = X2 = X3 = 0 using K -nearest neighbors. (a) Compute the Euclidean distance between each observation and the test point, X1 = X2 = X3 = 0. (b) What is our prediction with K = 1? Why? (c) What is our prediction with K = 3? Why? The following image shows a classification training dataset overlaid with different decision surfaces. (The background is red if a new point in that area will be classified as red, and the background is blue if a new point in that area will be classified as blue.) 45 4.5 4.5 40 40 4.0 3.5 3.5 3.5 30 30 30 25 25 2.5 20 20 20 15 15 15 10 10 10 All 3 decision surfaces are generated with K Nearest Neighbors, but the value of k (the number of neighbors considered) is different in each of the 3 images. Is the value of k increasing or decreasing as we move from the leftmost image to the rightmost image? O k increases as we move from left to right O k decreases as we move from left to rightIn the previous question, what parts of the image gives you information about the relative value of k? Why? Consider the data and decision surface map shown below. This map was produced from the data shown using K=5 and standard Euclidean distance. KNN fails to yield good decision boundaries for these data despite the two classes being clearly separated. Why do you think KNN fails on this dataset? Decision Surface