In the machine learning project, when the target variable is skewed, we need to use box-cox transformation to turn that into a normal distribution.

  1. But why do we need to do that? I mean, besides the linear regression, which model has the assumption that the target variable should belong to the normal distribution?
  2. If we use random forest that don't have any assumption on the data, do we have to transform the data?


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Data pre-processing is often a very important (if not the most important) step in a machine learning algorithm. Decisions trees are often an exception that they can work well without any pre-processing. But they may work better if you can identify some processes that might improve the quality of the decision detection.

As an example of other machine learning models: Linear Discriminant Analysis, or Quadratic Discriminant Analysis are both models that are explicitly calculated from the assumption of the distribution being a multivariate normal.

However, all that these models do is create either a 'linear' decision boundary or a 'quadratic' decision boundary to separate classes (in a classification problem)

This has been shown to give good results often when the data is not necessarily normally distributed; so my point being that just because a model assumes one thing that isn't necessarily true does not mean it won't still be a valid and effective way of generating accurate results.

Of course it might also fail miserably - herein lies the art of machine learning.

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