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Machine Learning (ML) MCQs | Page - 33
Dear candidates you will find MCQ questions of Machine Learning (ML) here. Learn these questions and prepare yourself for coming examinations and interviews. You can check the right answer of any question by clicking on any option or by clicking view answer button.
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Q. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very large value of C(C->infinity)?
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Q. SVM can solvelinearand non- linearproblems
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Q. The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N the number of features) that distinctly classifies the data points.
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Q. Hyperplanes are decision boundaries that help classify the data points.
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Q. SVMalgorithmsusea set of mathematical functions that are defined as thekernel.
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Q. In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.
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Q. In SVR we try to fit the error within a
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Q. Which of the following is true about Naive Bayes ?
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Q. Which of the following isnotsupervised learning?
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