1. Linear Regression is a machine learning algorithm based on _____.
ANSWER= B) supervised learning
Explain:- Linear Regression is a machine learning algorithm based on supervised learning.
2. Regression models a target prediction value based on _____.
ANSWER= B) independent variables
Explain:- Regression models a target prediction value based on independent variables.
3. regression technique finds out a linear relationship between x (input) and y(output) hence it is called as _________.
ANSWER= C) Linear Regression
Explain:- regression technique finds out a linear relationship between x (input) and y(output) hence it is called Linear Regression.
4. In Linear Regression RMSE stands for_________.
ANSWER= B) Read Mean Squared Error
Explain:- RMSE stands for Root Mean Squared Error
5.Root Mean Squared error give difference between_________.
ANSWER= B) predict value and true value
Explain:- RMSE stands for Root Mean Squared Error
6.________is an analytical approach to Linear Regression with a Least Square Cost Function..
ANSWER= C) Normal Equation
Explain:- Normal Equation is an analytical approach to Linear Regression with a Least Square Cost Function.
7. Linear Regression is mostly used for finding out the relationship between_____
ANSWER= C) both a and b
Explain:- It is mostly used for finding out the relationship between variables and forecasting
Question 4, answer is wrong.