which of the following is an attribute of supervised learning?

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