Knowledge Check 7_ ML Models_ ISYS7730

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2/11/24, 8:03 PM Knowledge Check 7: ML Models: ISYS7730.01|.02 Spring 2024 Data Analytics 3: In Practice [Beebe] https://bostoncollege.instructure.com/courses/1657214/quizzes/2429043 1/6 * Some questions not yet graded Due Feb 11 at 11:59pm Points 15 Questions 7 Time Limit None Allowed Attempts 5 Instructions Attempt History Attempt Time Score KEPT Attempt 2 9 minutes 9 out of 15 * LATEST Attempt 2 9 minutes 9 out of 15 * Attempt 1 41 minutes 4.5 out of 15 * Correct answers will be available on Feb 12 at 8am. Score for this attempt: 9 out of 15 * This knowledge check is to help you assess if you need to review anything or ask follow-up questions to the TAs/professor. The last questions will ask you to use ChatGPT to create a graph in python. Before starting the quiz: Read this short article on how to create an effective prompt: Prompts to Try Asking ChatGPT (https://bostoncollege.instructure.com/courses/1657214/files/73222560?wrap=1) (https://bostoncollege.instructure.com/courses/1657214/files/73222560/download?download_frd=1) If you have not done so already, create a free account to use ChatGPT: Go to https://openai.com (https://openai.com) Click "Try ChatGPT" in the upper right corner of the page Click to Sign Up and enter the required information As an example, here's my conversation with ChatGPT to figure out how to get data from YouTube's public API into a pandas dataframe: My ChatGPT Conversation.pdf (https://bostoncollege.instructure.com/courses/1657214/files/73222534?wrap=1) (https://bostoncollege.instructure.com/courses/1657214/files/73222534/download?download_frd=1) KNOWLEDGE CHECK 7: ML MODELS KNOWLEDGE CHECK 7: ML MODELS Class 8: Predictive Modeling (2/7) Take the Quiz Again
2/11/24, 8:03 PM Knowledge Check 7: ML Models: ISYS7730.01|.02 Spring 2024 Data Analytics 3: In Practice [Beebe] https://bostoncollege.instructure.com/courses/1657214/quizzes/2429043 2/6 Submitted Feb 11 at 8:02pm This attempt took 9 minutes. 2 / 2 pts Question 1 Overfitting is bad because The model does not perform well on the data set you trained it on. The model might perform poorly when applied on a different dataset (out-of- sample). The model gives large coefficient values. The model takes too long to run the complicated models. Overfitting is a problem because, while the model performs well on the data set you trained it on, it does not perform similarly well on other datasets. 3 / 3 pts Question 2 You are running a classification model to predict which of three animals (cat, horse, dog) are in each photo in a dataset. You get the following confusion matrix: Actual Actual Actual Cat Horse Dog Predicted Cat 85 8 18 Predicted Horse 5 62 37 Predicted Dog 30 3 100
2/11/24, 8:03 PM Knowledge Check 7: ML Models: ISYS7730.01|.02 Spring 2024 Data Analytics 3: In Practice [Beebe] https://bostoncollege.instructure.com/courses/1657214/quizzes/2429043 3/6 To 2 decimal places, what is the accuracy of your model? (Enter your answer as a decimal value, not a percentage. So if your answer was, for example, 50/60, enter 0.83 not 83.33%.) 0.71 Sum the correct entries divided by the total entries. 0 / 1 pts Question 3 Incorrect Incorrect Which statement is true about the following command: sklearn.model_selection.train_test_split( features, outcome, test_size = 0.33) If my dataset has 300 observations, this command will assign the first 200 observations to the train set and the rest of the observations to the test set. The output to this function consists of two outputs: train and test subsets. If my dataset has 300 observations, this command will create a train set of 100 observations. This command splits features and outcome into test and train features and outcome
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