Variance stabilizing transformations in machine learning

Variable transformation in machine learning

You’ve probably heard that before training machine learning models, data scientists transform random variables to change their distribution into something closer to the normal distribution.

But, why do we do this? Which variables should we transform? Which transformations should we use? And, do we need to transform variables to train any machine learning algorithm?

These are the questions that we will address throughout this article. Let’s get started.

Feature engineering for machine learning

This article is the fourth in a series of articles on feature engineering for machine learning. You can learn more about how data scientists preprocess their data for machine learning at the following links:

1. Feature engineering for machine learning
2. Missing data imputation
3. Categorical variable encoding
4. Variable transformation (you are here)
5. Discretization
6. Feature Scaling
7. Feature creation
8. Python libraries for feature engineering
9. Excellent resources for learning about feature engineering