APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Line
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Plane
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Hyperline
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Hyperplane
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Detailed explanation-1: -In Machine Learning, a hyperplane is a decision boundary that divides the input space into two or more regions, each corresponding to a different class or output label. In a 2D space, a hyperplane is a straight line that divides the space into two halves.
Detailed explanation-2: -If you think of yourself as a point on the decision boundary, the number of (non-parallel nor anti-parallel) directions you could travel on the boundary will be its dimensions. With a line you can go forward or backward (which is anti-parallel to forward), so there is only one dimension.
Detailed explanation-3: -A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. If the decision surface is a hyperplane, then the classification problem is linear, and the classes are linearly separable. Decision boundaries are not always clear cut.