# MACHINE LEARNING

## APPLICATION OF SUPERVISED LEARNING

### LINEAR REGRESSION

 Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
In linear regression, the residual refers to the difference between the predicted Y and actual Y values.
 A True B False C Either A or B D None of the above
Explanation:

Detailed explanation-1: -The predicted value of Y is called the predicted value of Y, and is denoted Y’. The difference between the observed Y and the predicted Y (Y-Y’) is called a residual. The predicted Y part is the linear part. The residual is the error.

Detailed explanation-2: -In linear regression, a residual is the difference between the actual value and the value predicted by the model (y-ŷ) for any given point. A least-squares regression model minimizes the sum of the squared residuals.

Detailed explanation-3: -The difference between the actual value of the dependent variable y (in the sample date) and the predicted value of the dependent variable ^y obtained from the linear regression equation is called the error or residual.

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