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I am curious about how many ways can we normalize data in data processing step before we use it to train machine learning model, deep learning model and so on.

All I know is

  1. Z-score normalization = (data - mean)/variance.
  2. Min-Max normalization = (data - min)/(max - min)

Do we have other ways except these two that I know?

Ronakrit W.
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1 Answers1

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There are many ways to normalize the data prior to training a model, some depends on the task, data type (tabular, image, signals) and data distribution. You can find the most important ones in scikit-learn preprocessing subpackage:

To highlight few that I have been using consistently, Box-Cox or Yeo-Johnson transformation, where it is used when your feature's distribution is skewed. This will minimize the skewness through maximum likelihood.

Another normalization technique is called Robust Scaler that is can perform better than the Z-score normalization if your dataset contains many outliers as they can falsely influence the sample mean and variance.

Coderji
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