What is linear regression in Machine learning?

Question

Can anyone explain linear regression in Machine learning?

Answer 1

Linear regression in Machine Learning is a supervised algorithm and the most used regression algorithm. In simple words, linear regression means fitting the best fit line between independent and target variables with the least mean square error.

Before implementing linear regression, we should check whether the data is following these assumptions:

• Data should be linear
• No Multicollinearity
• No auto-correlation
• Homoskedasticity should be there

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Answer 2

Linear regression is a technique that is used in Machine Learning to predict the outcome of a variable based on the linearity of the input. That is, the predicted values will have a continuous range and not discrete. Continous values can be prices, time, etc., while discrete data includes things such as pets, headphones, and more. The process of regression is mostly used to forecast and determine relationships between two or more variables. The cost function is used to find out the actual learning rate by determining the error between the predicted value and the actual value. The concept of gradient descent is used to understand how big or small the learning rate has been in every step of the way. More so, it determines the number of steps that are needed for the algorithm to learn.

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