COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
categorical or discrete
|
|
|
numerical or continuous
|
|
|
Either A or B
|
|
|
None of the above
|
Detailed explanation-1: -So, regression is a machine learning technique where the model predicts the output as a continuous numerical value.
Detailed explanation-2: -A linear regression model is a conditional model in which the output variable is a linear function of the input variables and of an unobservable error term that adds noise to the relationship between inputs and outputs.
Detailed explanation-3: -Linear regression analysis rests on the assumption that the dependent variable is continuous and that the distribution of the dependent variable (Y) at each value of the independent variable (X) is approximately normally distributed.
Detailed explanation-4: -Regression analysis is a supervised machine learning process for estimating the relationships among different fields in your data, then making further predictions on numerical data based on these relationships.
Detailed explanation-5: -Since linear regression gives output as continuous values, so in such cases, we use mean squared error or r-squared metric to evaluate the model performance.