COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Surface
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Plane
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Hyperplane
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Hypersurface
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Detailed explanation-1: -Linear regression is a machine learning model that fits a hyperplane on data points in an m+1 dimensional space for a data with m number of features. A hyperplane is a plane whose number of dimension is one less than its ambient space.
Detailed explanation-2: -The line of best fit is an output of regression analysis that represents the relationship between two or more variables in a data set. An error term is a variable in a statistical model when the model doesn’t represent the actual relationship between the independent and dependent variables.
Detailed explanation-3: -Hyperplanes can also be used in regression tasks where the goal is to predict a continuous output value rather than a class label. In this case, the hyperplane represents the line of best fit that minimizes the sum of the squared errors between the predicted values and the true values.