The Splunk Machine Learning Toolkit App delivers new SPL commands, custom visualizations, assistants, and examples to explore a variety of ml concepts.
Each assistant includes end-to-end examples with datasets, plus the ability to apply the visualizations and SPL commands to your own data. You can inspect the assistant panels and underlying code to see how it all works.
ML Youtube Playlist http://tiny.cc/splunkmlvideos
ML Cheat Sheet http://tiny.cc/mlcheatsheet
* Predict Numeric Fields (Linear Regression): e.g. predict median house values.
* Predict Categorical Fields (Logistic Regression): e.g. predict customer churn.
* Detect Numeric Outliers (distribution statistics): e.g. detect outliers in IT Ops data.
* Detect Categorical Outliers (probabilistic measures): e.g. detect outliers in diabetes patient records.
* Forecast Time Series: e.g. forecast data center growth and capacity planning.
* Cluster Numeric Events: e.g. Cluster Hard Drives by SMART Metrics
For the Splunk Machine Learning Toolkit documention, see: http://docs.splunk.com/Documentation/MLApp/latest
This application may contain certain sample files and datasets, which are provided for your convenience only. Such files and datasets contain information and data compiled by third parties, and Splunk makes no representation or warranty that the data contained in such files and datasets are true, accurate, complete or sanitized.
You must install the Python for Scientific Computing Add-on before installing the Machine Learning Toolkit. Please download and install the appropriate version here:
Linux 64-bit: https://splunkbase.splunk.com/app/2882/
Linux 32-bit: https://splunkbase.splunk.com/app/2884/
Windows 64-bit: https://splunkbase.splunk.com/app/2883/
To install an app within Splunk Enterprise:
Log into Splunk Enterprise.
Next to the Apps menu, click the Manage Apps icon.
Click Install app from file.
In the Upload app dialog box, click Choose File.
Locate the .tar.gz or .tar file you just downloaded, then click Open or Choose.