Which of the following is a supervised learning problem? As the value of one attribute increases the value of the second attribute also increases. 7. These short solved questions or quizzes are provided by Gkseries. 36. Supervised Machine Learning. c. at least one output attribute. e. at least one input attribute. Supervised learning is a simpler method while Unsupervised learning is a complex method. Supervised learning problems can be further grouped into Regression and Classification problems. Classification in Data Mining Multiple Choice Questions and Answers for competitive exams. 8. 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. All of the above b. ouput attriubutes to be categorical. 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. require input attributes to take on numeric values. Introduction to Supervised Machine Learning Algorithms. d. ouput attriubutes to be categorical. Which of the following is a common use of unsupervised clustering? The biggest challenge in supervised learning is that Irrelevant input feature present training data could give inaccurate results. Both problems have as goal the construction of a succinct model that can predict the value of the dependent attribute from the attribute variables. 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. What does this value tell you? D.categorical attribute. B. hidden attribute. A) Grouping people in a social network. Supervised learning differs from unsupervised clustering in that supervised learning requires a. at least one input attribute. 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. As the value of one attribute decreases the value of the second attribute increases. c. at least one output attribute. b. input attributes to be categorical. In supervised learning , the data you use to train your model has historical data points, as well as the outcomes of those data points. The attributes are not linearly related. The majority of practical machine learning uses supervised learning. These short objective type questions with answers are very important for Board exams as well as competitive exams. (2.4) 8. Supervised learning differs from unsupervised clustering in that supervised learning requires Select one: a. Supervised Learning. C. input attribute. Supervised learning and unsupervised clustering both require which is correct according to the statement. d. require each rule to have exactly one categorical output attribute. d. input attributes to be categorical. A. output attribute. 4. 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. E.All of these. The correlation coefficient for two real-valued attributes is 0.85. 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