Supervised learning and unsupervised clustering both require which is correct according to the statement. C. input attribute. As the value of one attribute decreases the value of the second attribute increases. All of the above b. ouput attriubutes to be categorical. 36. Supervised learning differs from unsupervised clustering in that supervised learning requires a. at least one input attribute. d. require each rule to have exactly one categorical output attribute. Supervised Machine Learning. What does this value tell you? Which of the following is a supervised learning problem? The correlation coefficient for two real-valued attributes is 0.85. In supervised learning , the data you use to train your model has historical data points, as well as the outcomes of those data points. Supervised learning is a form of machine learning in which the input and output for our machine learning model are both available to us, that is, we know what the output is going to look like by simply looking at the dataset. The biggest challenge in supervised learning is that Irrelevant input feature present training data could give inaccurate results. The attributes are not linearly related. Supervised learning differs from unsupervised clustering in that supervised learning requires Select one: a. Both problems have as goal the construction of a succinct model that can predict the value of the dependent attribute from the attribute variables. c. at least one output attribute. c. require input attributes to take on numeric values. Introduction to Supervised Machine Learning Algorithms. The majority of practical machine learning uses supervised learning. D.categorical attribute. E.All of these. (2.4) 8. d. categorical attribute. Supervised learning vs. unsupervised learning The key difference between supervised and unsupervised learning is whether or not you tell your model what you want it to predict. All values are equals b. Classification in Data Mining Multiple Choice Questions and Answers for competitive exams. 7. d. input attributes to be categorical. As the value of one attribute increases the value of the second attribute also increases. Supervised learning is a simpler method while Unsupervised learning is a complex method. b. input attributes to be categorical. Supervised Learning. Which of the following is a common use of unsupervised clustering? F.None of these These short solved questions or quizzes are provided by Gkseries. 4. B. hidden attribute. A. output attribute. B) Predicting credit approval based on historical data C) Predicting rainfall based on historical data ... An attribute with lower mutual information should be preferred to other attributes. d. ouput attriubutes to be categorical. Supervised learning problems can be further grouped into Regression and Classification problems. 8. Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. These short objective type questions with answers are very important for Board exams as well as competitive exams. c. at least one output attribute. 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