Why is it important to transform the data into normal / Gaussian distribution when creating a linear regression model

user02Wl1 0
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I'm currently building my first regression model, and as we know that, owing to the limitations of the algorithm, we need to remove outliers and transform the distribution into a normal one.

I know that it's important and the ways to do it, but can someone please help me in understanding why exactly we need to do so? Why can't I work with a highly skewed distribution? Why does linear regression mandates this transformation in processing stage?

linear-regression
1 Answers

Classifier with and without Outliers in the data

Hope the above picture clears your doubt.

As LinearRegression model is optimized by passing through a path which has minimum squared error, due to outliers (which are abnormal data points or noise) our classifier may deviate and work poorly on the test data (much general data).

Minnikeswar Rao0
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