- Machine Learning: other versions:
- Setup and security
- Getting started with machine learning
- Anomaly detection
- Overview
- Concepts
- Configure anomaly detection
- API quick reference
- Supplied configurations
- Function reference
- Examples
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Detecting anomalous locations in geographic data
- Performing population analysis
- Altering data in your datafeed with runtime fields
- Adding custom URLs to machine learning results
- Handling delayed data
- Mapping anomalies by location
- Exporting and importing machine learning jobs
- Limitations
- Troubleshooting
- Data frame analytics
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 examples
editData frame analytics examples
editThese examples demonstrate how to use data frame analytics to derive useful insights from your data.
- Finding outliers in the eCommerce sample data
- Outlier detection example (Jupyter notebook)
- Predicting flight delays with regression analysis
- Predicting delayed flights with classification analysis
- Classification analysis example (Jupyter notebook)
- Language identification
- Feature importance for data frame analytics (Jupyter notebook)
Data frame analytics examples in blog posts
editThe blog posts listed below show how to get the most out of Elastic machine learning data frame analytics.
- Catching malware with Elastic outlier detection
- Benchmarking outlier detection results in Elastic machine learning
- Multilingual search using language identification in Elasticsearch
- Benchmarking binary classification results in Elastic machine learning
- Using Elastic supervised machine learning for binary classification
- Machine learning in cybersecurity – part 1: Training supervised models to detect DGA activity
- Machine learning in cybersecurity – part 2: Detecting DGA activity in network data
- Combining supervised and unsupervised machine learning for DGA detection
- Train, evaluate, monitor, infer: End-to-end machine learning in Elastic
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