This problem does not require the use of data mining software and focuses on knowledge of concepts and basic calculations. The dating web site Oollama.com requires its users to create profiles based on a survey in which they rate their interest (on a scale from 0 to 3) in five categories: physical fitness, music, spirituality, education, and alcohol consumption. A new Oollama customer, Erin O'Shaughnessy, has reviewed the profiles of 40 prospective dates and classified whether she is interested in learning more about them. Based on Erin's classification of these 40 profiles, Oollama has applied a logistic regression to predict Erin's interest in other profiles that she has not yet viewed. The resulting logistic regression model is as follows. Log odds of Interested = -0.920 + 0.325 x Fitness - 3.611 x Music + 5.535 x Education - 2.927 x Alcohol For the 40 profiles (observations) on which Erin classified her interest, this logistic regression model generates that following probability of Interested. Observation Interested Probability of Interested 35 1.000 21 1 0.999 29 1 0.999 25 1 0.999 39 1 0.999 26 1 0.990 23 1 0.981 33 1 0.974 M 0 0.882 24 1 0.882\f5 0 0.020 14 0 0.015 19 0 0.011 8 0 0.008 10 0 0.001 17 0 0.001 4 0 0.001 11 0 0.000 (a) Using a cutoff value of 0.5 to classify a profile observation as Interested or not, construct the confusion matrix for this 40-observation training set. Predicted Actual 0 1 18 2 2 18 Compute sensitivity, specificity, and precision measures and interpret them within the context of Erin's dating prospects. sensitivity 0.9 V specificity 0.9 precision 0.9 (b) Oollama understands that its clients have a limited amount of time for dating and therefore use decile-wise lift charts to evaluate their classification models. For the training data, what is the first decile lift resulting from the logistic regression model? Interpret this value. The first decile is the top 4 observations. The lift of the first decile is |40 * observations. The first decile of the logistic regression model |doubles vy the number of profiles that Erin is interested in versus random selection. (c) A recently posted profile has values of Fitness = 3, Music = 0, Education = 2, and Alcohol = 1. Use the estimated logistic regression equation to compute the probability of Erin's interest in this profile. (Round your answer to six decimal places.)