Supervised learning is a simpler method while Unsupervised learning is a complex method. e. at least one input attribute. The majority of practical machine learning uses supervised learning. c. require input attributes to take on numeric values. 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. c. at least one output attribute. A) Grouping people in a social network. In supervised learning , the data you use to train your model has historical data points, as well as the outcomes of those data points. As the value of one attribute decreases the value of the second attribute increases. All of the above b. ouput attriubutes to be categorical. 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. Both problems have as goal the construction of a succinct model that can predict the value of the dependent attribute from the attribute variables. As the value of one attribute increases the value of the second attribute also increases. 36. Classification in Data Mining Multiple Choice Questions and Answers for competitive exams. Supervised Machine Learning. E.All of these. Supervised Learning. c. at least one output attribute. A. output attribute. Supervised learning differs from unsupervised clustering in that supervised learning requires Select one: a. These short solved questions or quizzes are provided by Gkseries. The attributes are not linearly related. 4. d. input attributes to be categorical. 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. 8. d. ouput attriubutes to be categorical. Which of the following is a supervised learning problem? All values are equals b. These short objective type questions with answers are very important for Board exams as well as competitive exams. d. require each rule to have exactly one categorical output attribute. What does this value tell you? Which of the following is a common use of unsupervised clustering? a. unlike unsupervised learning, supervised learning can be used to detect outliers b. unlike unsupervised learning, supervised learning needs labeled data – c. unlike supervised leaning, unsupervised learning can form new classes d. there is no difference In asymmetric attribute Select one: a. b. input attributes to be categorical. Supervised learning and unsupervised clustering both require which is correct according to the statement. d. categorical attribute. B. hidden attribute. 7. (2.4) 8. F.None of these Supervised learning problems can be further grouped into Regression and Classification problems. D.categorical attribute. Introduction to Supervised Machine Learning Algorithms. Supervised learning differs from unsupervised clustering in that supervised learning requires a. at least one input 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. The correlation coefficient for two real-valued attributes is 0.85. The biggest challenge in supervised learning is that Irrelevant input feature present training data could give inaccurate results. C. input attribute. 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