What is the purpose of normalization in data preprocessing?

Instruction: Explain the concept and benefits of normalization in the context of preparing data for analysis.

Context: This question tests the candidate's understanding of data preprocessing techniques and their ability to explain the importance of normalization in statistical analysis and machine learning.

Updated

Official answer

Feature scaling changes the numerical representation of inputs so that units and magnitudes do not unintentionally dominate a scale-sensitive method. “Normalization” is ambiguous: specify whether you mean min-max scaling, standardization or scaling each observation to unit norm.

For example, with a training mean of 20 and standard deviation of 5, standardizing a value of 30 gives (30 - 20)/5 = 2. Use those same training statistics for later observations. Fit the scaler inside each training fold when cross-validating; fitting it on all data leaks evaluation information.

Scaling often matters for distance-based methods, regularized linear models and optimization. Ordinary decision-tree splits generally do not require standardization. Scaling does not make a distribution Gaussian, remove all outlier effects, ensure equal feature importance or establish fairness.

Choose the transformation for the data and estimator, define behavior for constant features, and compare results on untouched evaluation data.

Sources

StandardScaler; preprocessing methods.

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