Which statement about Support Vector Machines is TRUE?1 pointSupport Vector Machine models can be used for regression but not for classification. Support Vector Machine models are non-linear. Support Vector Machine models can be used for classification but not for regression.Support Vector Machine models rarely overfit on training data.
Question
Which statement about Support Vector Machines is TRUE?1 pointSupport Vector Machine models can be used for regression but not for classification. Support Vector Machine models are non-linear. Support Vector Machine models can be used for classification but not for regression.Support Vector Machine models rarely overfit on training data.
Solution
The true statement about Support Vector Machines is: "Support Vector Machine models are non-linear."
Support Vector Machines (SVMs) are supervised learning models used for classification and regression analysis. They are particularly well-suited for classification of complex but small- or medium-sized datasets.
Here's why the other statements are false:
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"Support Vector Machine models can be used for regression but not for classification." This is incorrect because SVMs can be used for both regression and classification tasks. In fact, they are widely used for classification problems.
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"Support Vector Machine models can be used for classification but not for regression." This is also incorrect. While SVMs are often used for classification, they can also be used for regression (SVR).
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"Support Vector Machine models rarely overfit on training data." This is not necessarily true. Like any model, SVMs can overfit if the parameters are not chosen carefully. Overfitting happens when the model is too complex and captures the noise along with the underlying pattern in data. It's always important to use techniques like cross-validation to ensure that your model generalizes well to unseen data.
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 machine?Question 2Answera.A linear model used to make predictions in supervised learningb.A neural network used in deep learningc.A tree-like model used to make predictions in supervised learningd.A clustering algorithm used in unsupervised learning
What is a non-linear support vector machine?Question 10Answera.A support vector machine that is sensitive to the scale of the input variablesb.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 uses a linear kernel 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.
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.
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