APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNINGHARD QUESTIONS
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Hypothesis function
|
|
|
Related regression
|
|
|
Linear Regression
|
|
|
none of these regression technique finds out a linear relationship between x (input) and y(output) hence it is called Linear Regression.
|
Detailed explanation-1: -Linear regression shows the linear relationship between the independent variable (X-axis) and the dependent variable (Y-axis), hence called linear regression. If there is only one input variable (x), then such linear regression is called simple linear regression.
Detailed explanation-2: -Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
Detailed explanation-3: -Also called simple regression or ordinary least squares (OLS), linear regression is the most common form of this technique. Linear regression establishes the linear relationship between two variables based on a line of best fit.