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
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