Support Vector Machine Quiz

What is the primary objective of a Support Vector Machine (SVM) algorithm?

• To reduce the size of the dataset by half.

• To find a hyperplane that classifies data points.

• To determine the dimensionality of the dataset.

• To increase the number of data features.

What kind of data is best suited for Linear SVM classification?

• Non-linearly separable data

• Data with multiple missing values

• Data that cannot be classified

• Linearly separable data

How does the dimension of a hyperplane change with the number of features in an SVM?

• It depends on the number of features.

• It changes only if features increase by more than three.

• It is always a straight line regardless.

• It remains a fixed dimension always.

What is the purpose of having a maximum margin in an SVM hyperplane?

• To maximize the distance between data points.

• To minimize the processing time.

• To increase the number of classes in the dataset.

• To reduce the number of features needed.

When is Nonlinear SVM Classification applied?

• When data is expensive to process.

• When the dataset is very small.

• When data is not linearly separable.

• When there are only two features available.

What is the primary objective of a Support Vector Machine (SVM) algorithm?

• To maximize the number of support vectors used.

• To minimize the data processing time in a linear fashion.

• To reduce the dimensionality of the dataset for easier evaluation.

• To find a hyperplane in an N-dimensional space that classifies data points distinctly.

When is Linear SVM Classification typically used?

• When the data requires nonlinear transformations for separation.

• For datasets with more than two classes and complex patterns.

• For datasets that can be separated into two classes using a single straight line.

• Any dataset regardless of its dimensionality and distribution.

What determines the dimensionality of the hyperplane in SVM?

• The number of features present in the dataset.

• The number of classes in the data set.

• The number of support vectors chosen.

• The complexity of the decision boundary required.

Which approach does a Support Vector Machine use for classification tasks?

• It employs a logarithmic scoring method to classify points.

• It uses a clustering algorithm to group data points.

• It uses the hyperplane with the maximum margin to separate classes.

• It applies randomized decision trees to partition the space.

What is meant by 'maximum margin' in the context of SVM?

• The total number of features exceeding data points.

• The greatest distance between the data points and the hyperplane.

• The length of the hyperplane itself in the feature space.

• The smallest possible hyperplane that contains all data points.