Cross-sectional data
Cross-sectional data are observations of multiple units or entities collected at a single point or period in time rather than repeatedly over time.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
Cross-sectional data contains observations of multiple units measured at or around the same period rather than repeatedly through time.
- How it is used
- Guides tabular machine-learning analysis when observations represent multiple units measured around one period.
- How deep it goes
- Explains non-IID dependencies, leakage, membership features, and grouped validation, but does not provide a complete implementation workflow.
- Examples
- Uses company country, operations, sector, and group-separated training and validation folds to illustrate dependency risks.
- What you can do after
- Diagnose leakage by encoding group relationships and separating related groups across validation folds.
Outlier Detection in Python
Cross-sectional data consists of observations on multiple entities without a temporal sequence being the primary organizing dimension.
- How it is used
- Organizes observations across entities when temporal ordering is not the primary analytical dimension.
- How deep it goes
- Defines the structure and distinguishes it from panel and time-series data, stopping short of analytical methods.
- Examples
- Uses baseball player records containing seasons, games, at-bats, runs, and hits.
- What you can do after
- Evaluate whether rows represent independent entities or observations arranged as measurements over time.
Read how Outlier Detection in Python explains cross-sectional data →
Related concepts
- panel dataincludes · Panel data consists of observations for multiple entities collected at multiple points in time, combining cross-sectional and longitudinal dimensions.
- predictive analyticsused by · Predictive analytics is the use of data and statistical or computational methods to estimate future or otherwise unknown outcomes.
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