Having started my journey in Data Science. I came across Mean Squared Error several times and realised how important that concept is.

Then I started working on project and needed to really understand what Mean Squared Error is ?

So what is Mean Squared Error ?

Simply,  let’s say you are building your model. And you will be using your model to estimate your values. That is , you use your training model to estimate the values of the test data. So how far your predictions deviate from the actual values of the test data is lets say the Mean Squared Error. But note, as the name suggests it is a mean/average of all the square of the errors or deviation

Why is it important?

You would always want to get a low Mean Squared Value as much as you can. The lower the MSE , the better and closer your model is to predicting the actual values of your test data.

I was working on a project and had to find out the simplest meaning of this all important Mean Squared Error

The simplest, straightforward and pratical explanation I found was on the Statistics How To website

You can check it here: Mean Squared Error explained

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