StandardScaler

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover StandardScaler?

Explains learned means and standard deviations, fitting, transformation, and leakage prevention without deriving the scaling formula.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

StandardScaler is a Scikit-learn transformer that standardizes numeric features using statistics learned from fitting data.

StandardScaler standardizes numeric features by learning each feature’s mean and standard deviation from fitting data, then applying those statistics to transform data. section 4.1.2

Use the transformer in two distinct steps:

Fit it on training data so the scaler learns transformation parameters. section 4.1.2
Transform new, validation, or test data with the fitted scaler, avoiding information leakage between datasets. section 4.1.2

from sklearn.preprocessing import StandardScaler
processing = StandardScaler().fit(X)

Instantiate StandardScaler and fit it to feature data, retaining the learned preprocessing parameters for later transformations. section 4.1.2

processing.fit_transform(X)

Fit and transform the same data in one operation when that workflow is appropriate; separate fitting and transformation when controlling dataset boundaries. section 4.1.2

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

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Data Without LabelsStandardScaler is a scikit-learn transformer that standardizes features by removing their means and scaling them to unit variance.

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Related concepts

standardizationimplements · 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.
sigmoidconfigures · A sigmoid is a smooth, bounded, S-shaped function that maps real-valued inputs between two limiting values, commonly zero and one.
preprocessingused by · Preprocessing is the preparation or transformation of data, content, or other inputs into a form suitable for a subsequent computational operation.
feature scalingimplements · Feature scaling is the transformation of numerical features to comparable magnitudes, ranges, or distributions for computation or model fitting.
language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
min-max normalizationalternative to · Min-max normalization is a feature-scaling technique that linearly maps numerical values to a specified interval using their minimum and maximum values.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.