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This PR adds a notebook that explains the properties of different class assignment objectives available for the machine learning data frame analytics classification jobs and gives pointers on when to choose which objective.

It intends to be linked in the "Concepts" section of the documentation and provide users with more insights than it is possible in the scope of "Concepts".

The rendered version of the jupyter notebook can be found here.

Closes #356.

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Mar 23, 2020
Sep 9, 2014


This is a collection of examples to help you get familiar with the Elastic Stack. Each example folder includes a README with detailed instructions for getting up and running with the particular example. The following information pertains to the examples repo as a whole.


Quick start

You have a few options to get started with the examples:

  • If you want to try them all, you can download the entire repo . Or, if you are familiar with Git, you can clone the repo. Then, simply follow the instructions in the individual README of the examples you're interested in to get started.

  • If you are only interested in a specific example or two, you can download the contents of just those examples - follow instructions in the individual READMEs OR you can use some of the options mentioned here.


See here

Example catalog

Below is the list of examples available in this repo:

Common Data Formats

Exploring Public Datasets

Examples using the Elastic Stack for analyzing public dataset.

Getting Started with Graph exploration

Alerting on Elastic Stack

Alerting lets you set up watches (or rules) to detect and alert on changes in your Elasticsearch data. Below is a list of examples watches that configured to detect and alert on a few common scenarios:

Machine learning

Search & API Examples

Security Analytics