Please use the table below for questions 1 through 3. Notice that Count column is NOT an attribute. It just tells how many times a row occurs in our database and status is our salary status target variable.
46K_50K senior 26K_30K junior 31K_35K junior 46K_50K junior 66K_70K senior 46K_50K junior 66K_70K senior 46K_50K senior 41K_45K junior 36K_40K senior 26K_30K junior
1. Given a data tuple having the values “systems”, “26_30”, and “46K_50K” for the attributes department, age, and salary, respectively, what would a naive Bayesian classification of the status?
2. Split your diabetes data into two parts for training and testing purposes. Namely, reserve last 10 rows of the diabetes_train.csv for the test set. Then fit a SVM classifier on the bigger portion of this data and test it on these 10 rows you had reserved.
3. Draw the ROC curve based on the table below and fill the empty columns based on threshold at each step.
4. Please use the data shown for questions below. (20 points)
a.) If h and c are selected as the initial centers for your k-means clustering, assign memberships for other points, and compute the means (centroids) of your initial clusters. You can use Manhattan distance.
b.) Based on the centroids you found above reassign the memberships by using Manhattan distance.
5. Given the distance matrix below answer the following questions. Notice that this is a distance matrix, meaning the distance between any pair of points can be found by checking the corresponding cell to them.
a.) Perform hierarchical clustering using single link measure for the above and draw the final dendrogram.
b.) Determine whether a point is core based on ε = 6 and minP ts = 2.
