Standardization
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.
Statistics Slam Dunk
Standardization transforms numeric variables to a common scale, commonly by subtracting the mean and dividing by the standard deviation.
- How it is used
- Enables fair comparisons of NBA measures across incompatible scales, seasons, and changing playing conditions.
- How deep it goes
- Defines z-scores, demonstrates grouped R calculations, compares four methods, and interprets standardized NBA results without formal proofs.
- Examples
- Uses payroll versus wins, season-level team statistics, and historical NBA scoring leaders.
- What you can do after
- Apply it via R to create and interpret season-grouped z-scores for comparable numeric analysis.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A standardized feature is a feature transformed to a normalized scale, commonly with a specified mean and standard deviation.
- How it is used
- Prepares continuous tabular features for classical and deep learning models when their value ranges differ substantially.
- How deep it goes
- Defines mean-and-standard-deviation rescaling and demonstrates pandas, Scikit-learn pipelines, and normalization layers without mathematical derivation.
- Examples
- Uses Auto MPG boxplots, synthetic binary classification, Airbnb NYC feature pipelines, and Kuala Lumpur real-estate columns.
- What you can do after
- Apply it via StandardScaler, pandas calculations, or normalization layers within a preprocessing and model-training workflow.
Ensemble Methods for Machine Learning
Standardization rescales numerical features so that each feature has a specified mean and dispersion, commonly zero mean and unit standard deviation.
- How it is used
- Uses feature scaling to place numerical inputs on comparable ranges before downstream regression or classification.
- How deep it goes
- Explains zero-mean unit-variance scaling, train-only fitting, and library implementation, while deferring broader discrete-feature strategies.
- Examples
- Uses AutoMPG, Bike Sharing, and Australian Credit Approval data to demonstrate numerical scaling alongside regression or categorical encoding.
- What you can do after
- Apply it via sklearn.preprocessing.StandardScaler without fitting preprocessing parameters on the held-out test set.
Read how Ensemble Methods for Machine Learning explains standardization →
Knowledge Graphs and LLMs in Action
Feature standardization transforms numerical features so that they have a specified mean and scale, commonly zero mean and unit variance.
- How it is used
- Prepares heterogeneous node features for downstream classification such as node classification or link prediction.
- How deep it goes
- Demonstrates fitting and reusing StandardScaler, but does not derive the transformation mathematically.
- Examples
- Uses node degree, centrality measures, graph embeddings, and the karate club network as concrete feature examples.
- What you can do after
- Apply it via StandardScaler to align training and prediction features before fitting and evaluating a classifier.
Read how Knowledge Graphs and LLMs in Action explains standardization →
Data Without Labels
Standardization is the transformation of numeric variables to a common scale, typically by centering them and scaling by their variability.
- How it is used
- Prepares differently scaled numeric features for distance-based clustering.
- How deep it goes
- Demonstrates feature selection, z-score and StandardScaler implementations, visualization, and k-means use.
- Examples
- Uses a vehicle dataset, feature scatter plots, and an elbow plot comparing one through nine clusters.
- What you can do after
- Apply standardization with scipy.stats.zscore or sklearn.preprocessing.StandardScaler before fitting k-means.
Related concepts
- standard deviationuses · Standard deviation is the nonnegative square root of variance and measures the typical dispersion of values around their mean in the original units.
- z-scoreproduces · A z-score is a signed standardized value expressing how many standard deviations an observation lies above or below a reference mean.
- 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.
- feature scalingkind of · Feature scaling is the transformation of numerical features to comparable magnitudes, ranges, or distributions for computation or model fitting.
- pipelineused by · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
- preprocessingkind of · Preprocessing is the preparation or transformation of data, content, or other inputs into a form suitable for a subsequent computational operation.
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