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Analyze data

These sections help you interpret the findings and visualizations created by DataRobot.

Topic Description
Data Quality Assessment Interpret a dataset's Data Quality Assessment results.
Data quality checks Read descriptions of each data quality check, as well as the logic DataRobot applies to detect, and often repair, common data quality issues.
Post data ingest analysis (EDA1)
Assess data quality with EDA Assess the quality of your data during each phase of Exploratory Data Analysis (EDA).
Analyze features using histograms Analyze the distribution of a feature's values and investigate outliers.
Analyze frequent values Look into the values that appear most and least frequently for a feature.
Feature details Interpret histograms, frequent values charts, and transformations.
EDA1 View summary statistics based on a sample of your data.
ESDA Interactively visualize, explore, and aggregate target, numeric, and categorical features on a map.
Fast EDA for large datasets Understand Fast Exploratory Data Analysis (EDA) for large datasets, and how to apply early target selection.
Over Time chart Review time-aware visualizations of how a feature changes over time feature (time-aware only).
Post modeling analysis (EDA2)
EDA2 View summary statistics based on the portion of the data used for EDA1, excluding rows that are also in the holdout data and rows where the target is N/A.
Feature Associations Interpret feature correlations.
Use data pipelines for ingest and transformation Learn how to create a reusable data pipeline to keep datasets cataloged inside DataRobot up-to-date and ready for experimentation, batch predictions, or automated retraining jobs.

Updated June 24, 2024