- Kibana Guide: other versions:
- Introduction
- Get started
- Set Up Kibana
- Discover
- Visualize
- Creating a Visualization
- Saving Visualizations
- Using rolled up data in a visualization
- Line, Area, and Bar charts
- Controls Visualization
- Data Table
- Markdown Widget
- Metric
- Goal and Gauge
- Pie Charts
- Coordinate Maps
- Region Maps
- Timelion
- TSVB
- Tag Clouds
- Heatmap Chart
- Vega Graphs
- Inspecting Visualizations
- Dashboard
- Canvas
- Graph data connections
- Machine learning
- Elastic Maps
- Code
- Infrastructure
- Logs
- APM
- Uptime
- SIEM
- Dev Tools
- Stack Monitoring
- Management
- Reporting from Kibana
- REST API
- Kibana plugins
- Limitations
- Release Highlights
- Breaking Changes
- Release Notes
- Developer guide
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Data frame analytics
editData frame analytics
editThe Elastic machine learning data frame analytics feature enables you to analyze your data using outlier detection algorithms and generate new indices that contain the results alongside your source data.
If you have a license that includes the machine learning features, you can create outlier detection data frame analytics jobs and view their results on the Analytics page in Kibana.
For more information about the data frame analytics feature, see Machine learning data frame analytics.
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