AuthThe Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.AvailabilityGenerally available since 6.3This functionality is generally available and ready for production usage.Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.
Get defaults and limits used by machine learning.
This endpoint is designed to be used by a user interface that needs to fully
understand machine learning configurations where some options are not
specified, meaning that the defaults should be used. This endpoint may be
used to find out what those defaults are. It also provides information about
the maximum size of machine learning jobs that could run in the current
cluster configuration.
One or more character filters. In addition to the built-in character filters, other plugins can provide more character filters. If this property is not specified, no character filters are applied prior to categorization. If you are customizing some other aspect of the analyzer and you need to achieve the equivalent of categorization_filters (which are not permitted when some other aspect of the analyzer is customized), add them here as pattern replace character filters.
filterobject[]
One or more token filters. In addition to the built-in token filters, other plugins can provide more token filters. If this property is not specified, no token filters are applied prior to categorization.
tokenizerstring | object
The name or definition of the tokenizer to use after character filters are applied. This property is compulsory if categorization_analyzer is specified as an object. Machine learning provides a tokenizer called ml_standard that tokenizes in a way that has been determined to produce good categorization results on a variety of log file formats for logs in English. If you want to use that tokenizer but change the character or token filters, specify "tokenizer": "ml_standard" in your categorization_analyzer. Additionally, the ml_classic tokenizer is available, which tokenizes in the same way as the non-customizable tokenizer in old versions of the product (before 6.2). ml_classic was the default categorization tokenizer in versions 6.2 to 7.13, so if you need categorization identical to the default for jobs created in these versions, specify "tokenizer": "ml_classic" in your categorization_analyzer.