- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Tutorial: Getting started with anomaly detection
- Advanced concepts
- API quick reference
- How-tos
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Mapping anomalies by location
- Adding custom URLs to machine learning results
- Anomaly detection jobs from visualizations
- Exporting and importing machine learning jobs
- Resources
- Data frame analytics
- Natural language processing
Data frame analytics
editData frame analytics
editUsing data frame analytics requires source data to be structured as a two dimensional "tabular" data structure, in other words a data frame. Transforms enable you to create data frames which can be used as the source for data frame analytics.
Data frame analytics enable you to perform different analyses of your data and annotate it with the results. Consult Setup and security to learn more about the license and the security privileges that are required to use data frame analytics.
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