Sir, Could you explain what is meant by ‘Maximum Likelihood Method’ and how this method is the right answer to find the best fit line for the data in linear regression?
Quiz on Linear Regression
Hello @Management718,
Before continuing, I would like you to go through this discussion here and here.
Now coming to what “Maximum Likelihood Estimation” means intuitively is,
In statistics, the likelihood function (often simply called likelihood) expresses how likely particular values of statistical parameters are for a given set of observations.
So Maximum Likelihood Estimation is the process of modelling a Probability Distribution function p(y | x) such that the previous observations (training dataset) is very highly likely.
Take it this way, we are modelling a probability distribution function such that when a x is given, the observed y has high probability, learning the whole manifold of the true distribution.
Lets take an example to understand it clearly,
| x | y |
|---|---|
| 1.0 | 7.0 |
| 1.5 | 10.5 |
| 2.0 | 12.0 |
| 2.5 | 14.5 |
Here, we will train our model by maximizing the likelihood of each of the y values when corresponding x values were given. Hence, “Maximum Likelihood Estimation”.
Note: You will get a better understanding when you get to Logistic Regression.
Hope this made things clearer for you.
Happy Learning 
Thanks
1 Like
I hope I’ve cleared your doubt. I ask you to please rate your experience here
Your feedback is very important. It helps us improve our platform and hence provide you
the learning experience you deserve.
On the off chance, you still have some questions or not find the answers satisfactory, you may reopen
the doubt.