This blog aims to explain the need & logic behind using the Mean Squared Error in Machine Learning, Deep Learning, or Data Science. Anyone who has an intermediate level of knowledge in this field must have a doubt that why to use mean squared error, why not to use mean absolute error. This blog will explain the significance of Mean squared Error with deep Mathematical concepts!
Did you ever try to find the difference between Mean Squared Error(MSE) & *Mean Absolute Error(MAE), *rather than just the square term? It doesn’t matter the answer to this question is yes or no in your case, this blog will guide you from the right approach and significance of MSE in any field.
Most of the people think that the reason of using MSE is to remove the negative term in the output by squaring it, which is one of the correct reason, but it is not the major reason of using MSE, because negative term is also removed in the Mean Absolute Error by taking the absolute value of the output.
The actual reason behind using the Mean Squared Error is the concept of Maximum Likelihood Estimation(MLE).
There are chances that many people from the readers of this blog may know about MLE, but also, there will be some who are not aware of the concept. Therefore, I would like to explain this concept first, because this is the base & actual reason behind the usage of MSE.
It is a simple process of maximizing our desired value in a probabilistic function through estimation, i.e. in a probabilistic function, we have to estimate the value of some parameters which will yield the maximum output of the probabilistic function which is our desire.
Note: In this significance of MSE, I am assuming the data distribution to be normal, data of discrete distribution, I will explain in my future blogs.
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