Stratified sampling
Stratified sampling divides a population into defined subgroups and selects observations from each to control or preserve subgroup representation in the sample.
Machine Learning System Design
Stratification is a sampling technique that divides data into groups and controls the amount selected from each group.
- How it is used
- Supports validation design, subgroup representation, and efficient sampling for large, imbalanced, or duplicate-heavy datasets.
- How deep it goes
- Defines group-based sampling and selection priorities, but does not detail algorithms or validation procedures.
- Examples
- Uses cross-country customer representation and fresh-versus-old temporal groups as concrete sampling cases.
- What you can do after
- Apply stratification by defining subgroups and controlling each group’s contribution to training or test data.
Read how Machine Learning System Design explains stratified sampling →
Inside Deep Learning: Math, Algorithms, Models
A sampling method that preserves class proportions across data splits or samples.
- How it is used
- Improves imbalanced-classification evaluation by preserving class ratios across train/test splits.
- How deep it goes
- Defines the ratio-preservation idea and its motivation but defers detailed class-imbalance treatment and implementation.
- Examples
- Uses a 99%-to-1% class example and a name-language classification dataset to contrast stratified and random splits.
- What you can do after
- Apply it via scikit-learn to create train/test splits whose class percentages mirror the original dataset.
Read how Inside Deep Learning: Math, Algorithms, Models explains stratified sampling →
Related concepts
- dataset splitused by · A dataset split is the division of a dataset into distinct subsets, typically assigned separate roles or distributed for independent storage or processing.
- samplingused by · Sampling is a procedure for selecting or generating observations, values, or outcomes from a larger population, signal, or probability distribution according to a specified method.
- evaluation datasetconfigures · An evaluation dataset is a collection of examples or cases designated to assess a model or system against defined criteria.
- multilabel classificationuses · Multilabel classification is a classification task in which each input may be assigned zero or more labels simultaneously.
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