btt002_week-4-assignment-2

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Industrial Engineering

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Dec 6, 2023

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UNIT 4 ASSIGNMENT Introduction to Linear Models Instructions The questions below will prepare you for future interviews as they relate to concepts discussed throughout the unit. You’ve practiced these concepts in the coding activities, exercises and coding portion of the assignment. Now, let’s formulate your programming into well-thought responses. Except as indicated, use this document to record all your assignment work and responses to any questions. At a minimum, you will need to turn in a digital copy of this document to your facilitator as part of your assignment completion. You may also have additional supporting documents that you will need to submit. Your facilitator will provide feedback to help you work through your findings. Note: Though your work will only be seen by those grading the course and will not be used or shared outside the course, you should take care to obscure any information you feel might be of a sensitive or confidential nature. Begin your assignment by completing the questions below. Directions to submit your work can be found on the assignment page. Information about the grading rubric is available on any of the course assignment pages online. Do not hesitate to contact your facilitator if you have any questions about the assignment. 1 Machine Learning Foundations Cornell University © 2022 Cornell University

Unit 4 Written Portion Logistic Regression Answer the questions below about linear models. Questions: 1. What is a linear model? What are the advantages and disadvantages of linear models? Answer here 2. What type of supervised learning problem is logistic regression best suited for? Give an example of a problem you would use a logistic regression model for. Explain what you are trying to predict. Answer here 3. Describe the training phase of a logistic regression model: explain the intuition behind using gradient descent algorithm and the use of loss functions. Answer here 4. Explain the purpose of using regularization when training a logistic regression model. Answer here 5. Explain which linear model and accompanying loss function you would use for a classification problem and for a regression problem. 2 Machine Learning Foundations Cornell University © 2022 Cornell University

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