Standardization › Limitations and Considerations

When applying standardization, keep in mind several important limitations:

• Sparse data: For sparse matrices, standardizing by subtracting the mean can convert the data into a dense format, which is inefficient and memory-intensive.
  • Tip: Use StandardScaler(with_mean=False) to avoid densifying the matrix.
• Outliers: Standardization is sensitive to outliers, as extreme values can disproportionately affect the mean and standard deviation, potentially distorting the mapping.
• Categorical variables: Do not standardize one-hot encoded or ordinal categorical variables, as this can destroy their intended meaning.
• Target variable: In most cases, avoid standardizing the target variable unless there is a specific reason to do so.

Be aware of common pitfalls:

• Data leakage: Fitting the scaler on the entire dataset (including the test set) introduces information from the test data into the training process, leading to overly optimistic performance estimates.
  • Best practice: Always fit the scaler only on the training data, then use it to transform both the training and test sets.
• Inconsistent scaling: Using different scalers for training and test data can result in mismatched feature distributions.
• Over-standardization: Standardizing features that are already normalized can be unnecessary or even harmful.
• Categorical confusion: Standardizing categorical variables that should remain discrete can undermine their interpretability and utility.