Please this is midterm exam take from home so you can not cheat from chat gpt or any AI tools write from scratch and you need my school gmail and password to log inehales/grls-parasite-study |...

Please this is midterm exam take from home so you can not cheat from chat gpt or any AI tools write from scratch and you need my school gmail and password to log in







ehales/grls-parasite-study | Workspace | data.world








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CS634/482 Midterm Take at Home - Spring 2023 NOTE: Please do not forget to upload the take at home problem in Canvas. The deadline is the beginning of your in-person midterm exam - most classes this is 5.59pm of the day of the exam. Obviously you need to share the python notebook as you do with each assign- ment with the grader. Hardcopies of pdf prints of the notebook will not be accepted or graded. No points will be awarded to submissions that resemble code wise or answers wise and the students will be referred to the Dean of Students. Similarly, if students are found to collaborate in answering this exam. For this problem ONLY: You are ONLY allowed to look at the course site and your textbooks quoted in the syllabus or your handwritten notes. Take-at-home problem (45 points total) You are applying for a position at the data science team of USDA and you are given data associated with determining appropriate parasite treat- ment of canines. The suggested treatment options are determined based on a logistic regression model that predicts if the canine is infected with a parasite. The data is given in the site: https://data.world/ehales/grls-parasite- study/workspace/file?filename=CBC_data.csv Login using you University Google account to access the data and the description that includes a paper on the study (you dont need to read the paper to solve this problem necessarily). Your target variable ? column is titled parasite_status. Question 1 - Feature Engineering (5 points) In this step you outline the following as potential features (this is a limited example - we can have many features as in your programming exercise below). Write the posterior probability expressions: ?(? = 1|x) ?(? = 0|x) for the problem you are given to solve. 1 Question 2 Decision Boundary (5 points) Write the expression for the decision boundary assuming that ?(? = 1) = ?(? = 0) Question 3 Training - Loss function (5 points) Write the expression of the loss as a function of w that makes sense for you to use in this problem. ??? = NOTE: The loss will be a function that will include this function: ?(?) = 11 + ?−? Question 4 Training - Gradient (5 points) Write the expression of the gradient of the loss with respect to the parameters - show all your work. ∇w??? = Question 5 - Imbalanced dataset (10 points) You are now told that in the dataset ?(? = 0) >> ?(? = 1) Can you comment if the accuracy of Logistic Regression will be affected by such imbalance? Question 6 - SGD (15 points) The interviewer was impressed with your answers and wants to test your pro- gramming skills. 1. Use the dataset to train a logistic regressor that will predict the target variable ?. 2 2. Report the harmonic mean of precision (p) and recall (r) i.e the metric called ?1 score that is calculated as shown below using a test dataset that is 20% of each group. Plot the ?1 score vs the index ?. ?1 = 2 ?−1 + ?−1 Your code includes hyperparameter optimization of the learning rate and mini batch size. Please learn about cross validation which is a splitting strategy for tuning models here. 3 https://en.wikipedia.org/wiki/F-score https://en.wikipedia.org/wiki/F-score https://scikit-learn.org/stable/modules/cross_validation.html CS634/482 Midterm Take at Home - Spring 2023 Take-at-home problem (45 points total) Question 1 - Feature Engineering (5 points) Question 2 Decision Boundary (5 points) Question 3 Training - Loss function (5 points) Question 4 Training - Gradient (5 points) Question 5 - Imbalanced dataset (10 points) Question 6 - SGD (15 points)