The correct answer is: A. a plane with 1 dimensional fewer than number of input attributes.
A hyperplane is a flat surface in a higher-dimensional space. In a geometrical model of machine learning, a hyperplane is a plane that separates the data points into two classes. The number of input attributes is the number of dimensions in the space. A hyperplane with 1 dimensional fewer than number of input attributes is a plane that separates the data points into two classes in a space with one dimension fewer than the number of input attributes. This is the most common type of hyperplane used in machine learning.
Option B is incorrect because a plane with 2 dimensional fewer than number of input attributes would be a line. A line is not a flat surface, so it cannot be used to separate data points into two classes.
Option C is incorrect because a plane with 1 dimensional more than number of input attributes would be a plane that separates the data points into two classes in a space with one dimension more than the number of input attributes. This is not a common type of hyperplane used in machine learning.
Option D is incorrect because a plane with 2 dimensional more than number of input attributes would be a plane that separates the data points into two classes in a space with two dimensions more than the number of input attributes. This is not a common type of hyperplane used in machine learning.