MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Regression predictive modeling is the task of approximating a mapping function (f) from input variables (X) to a continuous output variable (y). A continuous output variable is a real-value, such as an integer or floating point value. These are often quantities, such as amounts and sizes.

Detailed explanation-2: -In regression the output is C) Continuous. No variable can be measured with infinite precision in practise.

Detailed explanation-3: -Linear regression gives you a continuous output, but logistic regression provides a constant output. An example of the continuous output is house price and stock price.

Detailed explanation-4: -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-5: -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.