![]() If you cannot collect more training data, create fewer groups of data using the by clause, giving you more data points per group. Aim for fitted distributions to have a cardinality (training dataset size) of at least 50.The accuracy of the anomaly detection for DensityFunction depends on the quality and the size of the training dataset, how accurately the fitted distribution models the underlying process that generates the data, and the value chosen for the threshold parameter.įollow these guidelines to make your models perform more accurately: ![]() Using the DensityFunction algorithm requires running version 1.4 or higher of the Python for Scientific Computing add-on. The DensityFunction algorithm supports the following continuous probability density functions: Normal, Exponential, Gaussian Kernel Density Estimation (Gaussian KDE), and Beta distribution. DensityFunction allows for grouping of the data using the by clause, where for each group a separate density function is fitted and stored. This algorithm supports incremental fit. The DensityFunction algorithm provides a consistent and streamlined workflow to create and store density functions and utilize them for anomaly detection. Splunk Cloud Platform customers need to create a support ticket to have this app installed.Īnomaly detection algorithms detect anomalies and outliers in numerical or categorical fields. The Splunk GitHub for Machine learning app provides access to custom algorithms and is based on the Machine Learning Toolkit open source repo. Splunk Cloud Platform customers can also use GitHub to add more algorithms via an app. to also learn about new machine learning algorithms, solve custom uses cases through sharing and reusing algorithms, and help fellow users of the MLTK. Join the Splunk Community for MLTK on GitHub. On-prem customers looking for solutions that fall outside of the 30 native algorithms can use GitHub to add more algorithms. You can also base your algorithm on over 300 open source Python algorithms from scikit-learn, pandas, statsmodel, numpy and scipy libraries available through the Python for Scientific Computing add-on in Splunkbase.įor information on how to import an algorithm from the Python for Scientific Computing add-on into the Splunk Machine Learning Toolkit, see the ML-SPL API Guide. The algorithms listed here and in the ML-SPL Quick Reference Guide are available natively in the Splunk Machine Learning Toolkit. ![]() This document is also offered in Japanese.ĭownload the ML-SPL Performance App for the Machine Learning Toolkit to use performance results for guidance and benchmarking purposes in your own environment.Įxtend the algorithms you can use for your models For information on the steps taken by these commands, see Understanding the fit and apply commands.įor information on using the score command, see Scoring metrics in the Machine Learning Toolkit.ĭownload the Machine Learning Toolkit Quick Reference Guide for a handy cheat sheet of current ML-SPL commands and machine learning algorithms available in the Splunk Machine Learning Toolkit. The MLTK supported algorithms use the fit and apply commands. ![]() You can find more examples for these algorithms on the scikit-learn website. Details for each algorithm are grouped by algorithm type including Anomaly Detection, Classifiers, Clustering Algorithms, Cross-validation, Feature Extraction, Preprocessing, Regressors, Time Series Analysis, and Utility Algorithms. The Splunk Machine Learning Toolkit (MLTK) supports all of the algorithms listed here. Algorithms in the Machine Learning Toolkit
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