StandardScaler
StandardScaler is a scikit-learn preprocessing transformer that centers each feature using its fitted mean and scales it using its fitted standard deviation.
Data Without Labels
StandardScaler is a scikit-learn transformer that standardizes features by removing their means and scaling them to unit variance.
- How it is used
- Prepares features for k-means clustering and PCA, including distance calculations that might otherwise favor larger-valued dimensions.
- How deep it goes
- Defines mean subtraction and standard-deviation scaling, then demonstrates fit_transform; defers comparisons with other scaling techniques.
- Examples
- Uses scaled vehicle features for an elbow-method clustering workflow and scaled iris features for covariance and PCA calculations.
- What you can do after
- Apply it via scikit-learn to transform feature columns before fitting k-means or calculating PCA inputs.
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.
- How it is used
- Standardizes numeric features before applying downstream machine-learning workflows.
- How deep it goes
- Explains learned means and standard deviations, fitting, transformation, and leakage prevention without deriving the scaling formula.
- Examples
- Uses StandardScaler().fit(X), transform(X), and fit_transform(X) as the core examples.
- What you can do after
- Apply StandardScaler to learn feature statistics and transform data without mixing training and evaluation information.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains StandardScaler →
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.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.