Select the TRUE statement regarding the cost function for SVMs:1 pointSVMs use the Hinge Loss function as a cost functionSVMs use a loss function that penalizes vectors prone to misclassificationSVMs do not use a cost function. They use regularization instead of a cost function.SVMs use same loss function as logistic regression
Question
Select the TRUE statement regarding the cost function for SVMs:1 pointSVMs use the Hinge Loss function as a cost functionSVMs use a loss function that penalizes vectors prone to misclassificationSVMs do not use a cost function. They use regularization instead of a cost function.SVMs use same loss function as logistic regression
Solution
The true statement regarding the cost function for SVMs is: "SVMs use the Hinge Loss function as a cost function".
Here's why:
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SVMs do use a cost function, and it's called the Hinge Loss function. This function is used to calculate the error of a prediction in SVMs.
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While it's true that SVMs use a loss function that penalizes vectors prone to misclassification, this statement is not specific to SVMs. Many machine learning algorithms use loss functions that penalize errors.
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SVMs do use a cost function, so the statement "SVMs do not use a cost function. They use regularization instead of a cost function" is false. Regularization is a technique used to prevent overfitting, and it's used in addition to a cost function, not instead of it.
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The statement "SVMs use same loss function as logistic regression" is also false. Logistic regression uses the log loss function, not the hinge loss function.
Similar Questions
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 objective of a Support Vector Machine (SVM)?Answer areaTo maximize the distance between the decision boundary and the nearest data points of any classTo minimize the number of misclassified pointsTo maximize the number of support vectorsTo minimize the computational complexity
Question 4In which cases would we want to consider using SVM?
2.Question 2What is the objective of SVM in terms of hyperplanes?1 pointChoose the hyperplane that’s closest to one of the two classes.Minimize the distance between hyperplane and the support vectors.Find the hyperplane of the lowest dimension.Choose the hyperplane that represents the largest margin between the two classes.
(True/False) A large c term will penalize the SVM coefficients more heavily.1 pointTrueFalse
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