What characterize is hyperplance in geometrical model of machine learning?

Solution(By Examveda Team)

What characterizes a hyperplane in the geometrical model of machine learning?

Option A: A plane with 1 dimensional fewer than the number of input attributes
In the geometrical model of machine learning, a hyperplane is a flat subspace of one dimension less than the number of input attributes or features. It separates data points in space, and this separation is achieved with one dimension less than the original feature space. So, Option A accurately characterizes a hyperplane.

Option B: A plane with 2 dimensional fewer than the number of input attributes
Option B describes a hyperplane as having two dimensions fewer than the number of input attributes, which is not a correct characterization. A hyperplane typically has one dimension less than the feature space.

Option C: A plane with 1 dimensional more than the number of input attributes
Option C suggests that a hyperplane has one dimension more than the number of input attributes, which is not accurate in the geometrical model of machine learning.

Option D: A plane with 2 dimensional more than the number of input attributes
Option D describes a hyperplane as having two dimensions more than the number of input attributes, which is not a correct characterization. A hyperplane typically has one dimension less than the feature space.

In summary, a hyperplane in the geometrical model of machine learning is characterized by being a plane with one dimension fewer than the number of input attributes, as described in Option A.