Hyperplanes are decision boundaries that help classify the data points.

The correct answer is: TRUE

A hyperplane is a flat surface in a higher dimensional space. In the context of machine learning, hyperplanes are used as decision boundaries to classify data points.

A hyperplane can be defined as a set of points that satisfy a linear equation. For example, the equation $x + y = 1$ defines a hyperplane in two dimensions.

Hyperplanes can be used to classify data points by finding the hyperplane that separates the data points into two classes. The data points on one side of the hyperplane are classified as one class, and the data points on the other side of the hyperplane are classified as the other class.

Hyperplanes are a powerful tool for classification, and they are used in many different machine learning algorithms.

Here is a brief explanation of each option:

• Option A: Hyperplanes are decision boundaries that help classify the data points. This is true, as explained above.
• Option B: Hyperplanes are not decision boundaries that help classify the data points. This is false, as explained above.

I hope this explanation is helpful. Let me know if you have any other questions.