How deeply does Machine Learning System Design cover feature scaling?

Identifies scaling as a numeric transformation and contrasts its model-dependent value without deriving procedures or formulas.

Feature scaling

As explained in Machine Learning System Design

Feature scaling is the transformation of numerical input features to comparable numerical ranges or scales.

Feature scaling transforms numerical inputs into comparable ranges or scales. It commonly appears as a preprocessing step before a model, alongside feature engineering such as one-hot encoding. section 10.1.1 section 11.1.2

Use scaling with attention to the model and the surrounding pipeline:

Include scaling consistently in the model pipeline so preprocessing forms part of the ordered path from input data to predictions. section 10.1.1
Treat scaling as one option among numeric transformations, alongside normalization and mathematical functions such as logarithms. section 11.1.2
Do not assume scaling improves every model: gradient boosting metrics may remain unchanged after monotonic transformations because decision trees are invariant to them. section 11.1.2

A simple classifier pipeline

A logistic regression classifier can be enhanced with a feature scaling step, producing a model pipeline that may also include one-hot encoding for categorical variables. section 10.1.1

How Machine Learning System Design covers it

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Also explained in

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Outlier Detection in PythonA preprocessing technique that transforms feature values so their numerical ranges or dispersions are comparable.
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Ensemble Methods for Machine LearningFeature scaling transforms input variables so their numerical magnitudes are placed on comparable scales.

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Related concepts

min-max normalizationimplemented by · Min-max normalization is a feature-scaling technique that linearly maps numerical values to a specified interval using their minimum and maximum values.
RobustScaleralternative to · RobustScaler is a scikit-learn transformer that scales features using robust statistics such as the median and interquartile range.
standardizationincludes · Standardization is the transformation of numerical data by centering and scaling it to a common reference, typically to a mean of zero and a standard deviation of one.
preprocessingpart of · Preprocessing is the preparation or transformation of data, content, or other inputs into a form suitable for a subsequent computational operation.
genetic algorithmused by · A genetic algorithm is an evolutionary optimization method that improves a population of candidate solutions through fitness-based selection and variation, often including recombination and mutation.
StandardScalerimplemented by · StandardScaler is a scikit-learn preprocessing transformer that centers each feature using its fitted mean and scales it using its fitted standard deviation.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.