DQuestion 1 Which of the following is true about independent and dependent varia
ID: 332840 • Letter: D
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DQuestion 1 Which of the following is true about independent and dependent variables? Independent variables must be numerical quantities and dependent variables may be numerical or categorical quantities. None of these are correct. Dependent variables must be categorical quantities and independent variables may be numerical or categorical quantities. In supervised learning the inputs to predictive models are dependent variables and the predicted values are independent variables. In supervised learning the inputs to predictive models are independent variables and the predicted values are dependent variables. DQuestion 2 Which of the following is the most accurate statement about supervised and unsupervised learning? For both unsupervised learning and supervised learning we predict a response variable. None of these are correct. Both supervised and unsupervised learning partition a data set into a training data set, a validation data set, and optionally, a test data set. Supervised learning models target variables as functions of predictors; unsupervised learning identifies patterns in data. In unsupervised learning we partition the data set provided for learning and use one partition to validate the learned model; in supervised learning there is no reason to partition the data set Question 3 Why did Target want to know if a woman is pregnant, and how did they do this? (Week 3 Discussion article) Target understood people's purchasing habits change with major life changes. Target wanted to make product offers of interest to expectant mothers before other retailers could. All of these are correct. Target had a large amount of customer data that could be analyzed for signals that a woman is expecting. Target believed the right offers at the right time to expectant mothers would induce them to become reliable customers for many years. Question 4 Which is NOT an example of a business application of supervised learning? ldentifying products that customers tend to purchase together Predicting home sales pricesin a neighborhood. All of these are correct. Classifying credit card transactions as valid or fraudulent. Categorizing customers as likely or unlikely to respond to a promotional offer.Explanation / Answer
Answer 1 - (E) In supervised learning the inputs to predictive models are independent variables and predicted values are dependent variable
Answer 4 - (D) Classifying a credit card transactions as valid or fraudulent (Here the end result is known, hence some data is already tagged with correct answer)
Answer 7 - (E) Predicting home sales prices in neighborhood (Here the end result is unknown, hence no data is tagged with correct answer)
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