d. input attributes to be categorical. All values are equals b. 7. 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 of the above b. ouput attriubutes to be categorical. b. input attributes to be categorical. Supervised Learning. d. require each rule to have exactly one categorical output attribute. 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 increases the value of the second attribute also increases. F.None of these Supervised learning problems can be further grouped into Regression and Classification problems. e. at least one input attribute. Which of the following is a supervised learning problem? 4. Supervised learning differs from unsupervised clustering in that supervised learning requires a. at least one input attribute. 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. Supervised Machine Learning. Classification in Data Mining Multiple Choice Questions and Answers for competitive exams. c. require input attributes to take on numeric values. B. hidden attribute. 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. 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. Supervised learning and unsupervised clustering both require which is correct according to the statement. Supervised learning differs from unsupervised clustering in that supervised learning requires Select one: a. These short solved questions or quizzes are provided by Gkseries. (2.4) 8. E.All of these. 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. As the value of one attribute decreases the value of the second attribute increases. 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. Which of the following is a common use of unsupervised clustering? c. at least one output attribute. Supervised learning is a simpler method while Unsupervised learning is a complex method. D.categorical attribute. 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. Introduction to Supervised Machine Learning Algorithms. These short objective type questions with answers are very important for Board exams as well as competitive exams. A) Grouping people in a social network. The majority of practical machine learning uses supervised learning. What does this value tell you? The attributes are not linearly related. 36. 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. d. ouput attriubutes to be categorical. c. at least one output attribute. 8. A. output attribute. C. input attribute. 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