How deeply does Ensemble Methods for Machine Learning cover feature scaling?

Explains scale-driven weight distortion and demonstrates library-based preprocessing, but does not detail scaling formulas.

Feature scaling

As explained in Ensemble Methods for Machine Learning

Feature scaling transforms input variables so their numerical magnitudes are placed on comparable scales.

Feature scaling transforms input variables so their numerical magnitudes are comparable. This matters when interpreting linear models: a wide-ranging variable such as salary can dominate a narrower variable such as age, distorting the apparent importance of model weights. section 9.1.3

Use scaling to support fairer learning and interpretation:

Place features in the same numerical range before training a robust linear model. section 9.1.3
Interpret weights cautiously when features retain substantially different magnitudes. section 9.1.3
Pair scaling with sparse linear models when comparing which features receive zero or nonzero weights. section 9.1.3

Breast-cancer diagnosis

A logistic-regression workflow loads breast-cancer data, applies StandardScaler to X, trains the model, and evaluates predictions. Scaling makes the learned feature weights more meaningfully comparable.

How Ensemble Methods for Machine Learning covers it

Cover of Ensemble Methods for Machine Learning

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Also explained in

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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 Ensemble Methods for Machine Learning. The section links point to the parts of the book it draws on.