I am a beginner in machine learning and basically every blog says to check if the data is normally distributed. Otherwise we can use log transformation or BoxCox. I wanted to know
- Does Gaussian - like means that the plot looks bell shaped but tests used to confirm it fails?
- For which methods of regression and classification, the data should be Gaussian or Gaussian like?
- Which methods (again in classification and regression) does not need Gaussian distribution?
According to this link, we do not have to worry about data distribution unless it is LDA or QDA.
So, what do you guys suggest?