Add a custom algorithm to the Machine Learning Toolkit overview

To add a custom algorithm to the Splunk Machine Learning Toolkit, you must register the algorithm in the MLTK app, create a Python script file for the algorithm, and write a Python algorithm class. The algorithm class must implement certain methods which are outlined in the BaseAlgo class in $SPLUNK_HOME/etc/apps/Splunk_ML_Toolkit/bin/base.py.

For information about the algorithms packaged with the Splunk Machine Learning Toolkit, see Algorithms in the Machine Learning Toolkit in the User Guide.

Coding is required to add a custom algorithm to the Splunk Machine Learning Toolkit. Development experience is an asset.

Custom algorithm examples

You can view end-to-end examples for the following custom algorithms:

• Correlation Matrix
• Agglomerative Clustering
• Support Vector Regressor
• Savitzky-Golay Filter