Transforming Skewed Data for Machine Learning
Skewed data is common in data science; skew is the degree of distortion from a normal distribution. For example, below is a plot of the house prices from Kaggle’s House Price Competition that is right skewed, meaning there are a minority of very large values.
Why do we care if the data is skewed? If the response variable is skewed like in Kaggle’s House Prices Competition, the model will be trained on a much larger number of moderately priced homes, and will be less likely to successfully predict the price for the most expensive houses. The concept is the same as training a model on imbalanced categorical classes. If the values of a certain independent variable (feature) are skewed, depending on the model, skewness may violate model assumptions (e.g. logistic regression) or may impair the interpretation of feature importance.
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