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What is a support vector machine?Select one:a.A clustering algorithm used in unsupervised learningb.A linear model used to make predictions in supervised learningc.A tree-like model used to make predictions in supervised learningd.A neural network used in deep learning

Question

What is a support vector machine?Select one:a.A clustering algorithm used in unsupervised learningb.A linear model used to make predictions in supervised learningc.A tree-like model used to make predictions in supervised learningd.A neural network used in deep learning

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Solution

A support vector machine is a linear model used to make predictions in supervised learning. So, the correct answer is b. A linear model used to make predictions in supervised learning.

Similar Questions

What is a support vector regression machine?Question 1Answera.A support vector machine that is sensitive to the presence of outliersb.A support vector machine that is sensitive to the scale of the input variablesc.A support vector machine that is used for regressiond.A support vector machine that is used for classification

What is a support vector in the context of support vector machines?Question 7Answera.A data point that is used to define the decision boundaryb.A data point that lies on the margin boundaryc.A data point that maximizes the margind.A data point that is classified correctly

What is a linear support vector machine?Question 6Answera.A support vector machine that uses a linear kernel functionb.A support vector machine that uses a non-linear kernel functionc.A support vector machine that is sensitive to the presence of outliersd.A support vector machine that is sensitive to the scale of the input variables

hat are Support Vector Machines (SVMs)?1 pointSVMs generate support vector points in order to minimize the loss function.SVMs attempts to bring support vector points closer together.SVMs maximally separates the closest support vector points.SVMs remove support vector points in order to minimize the loss function.

What is the main objective of support vector machines (SVM)?Review LaterTo model complex decision boundaries in high-dimensional data.To handle nonlinear relationships between variables.To maximize the margin between data points of different classes.To introduce nonlinearity to neural networks.

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