d. input attributes to be categorical. d. require each rule to have exactly one categorical output attribute. A) Grouping people in a social network. c. require input attributes to take on numeric values. Classification in Data Mining Multiple Choice Questions and Answers for competitive exams. d. 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. 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. c. at least one output attribute. F.None of these 36. The majority of practical machine learning uses supervised learning. A. output attribute. As the value of one attribute decreases the value of the second attribute increases. As the value of one attribute increases the value of the second attribute also increases. What does this value tell you? Introduction to Supervised Machine Learning Algorithms. The biggest challenge in supervised learning is that Irrelevant input feature present training data could give inaccurate results. c. at least one output attribute. Supervised Machine Learning. These short solved questions or quizzes are provided by Gkseries. C. input attribute. The attributes are not linearly related. Supervised learning is a simpler method while Unsupervised learning is a complex method. 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. 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. D.categorical attribute. Which of the following is a supervised learning problem? Both problems have as goal the construction of a succinct model that can predict the value of the dependent attribute from the attribute variables. Which of the following is a common use of unsupervised clustering? d. categorical attribute. e. at least one input attribute. All values are equals b. Supervised learning differs from unsupervised clustering in that supervised learning requires a. at least one input attribute. Supervised learning problems can be further grouped into Regression and Classification problems. 7. Supervised Learning. The correlation coefficient for two real-valued attributes is 0.85. All of the above b. ouput attriubutes to be categorical. 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. E.All of these. B. hidden attribute. (2.4) 8. 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. These short objective type questions with answers are very important for Board exams as well as competitive exams. Supervised learning and unsupervised clustering both require which is correct according to the statement. Supervised Machine Learning is defined as the subfield of machine learning techniques in which we used labelled dataset for training the model, making prediction of the output values and comparing its output with the intended, correct output and then compute the errors to modify the model accordingly. 4. Supervised learning differs from unsupervised clustering in that supervised learning requires Select one: a. 8. 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