Search commands for machine learning
The Splunk Machine Learning Toolkit contains several custom search commands, referred to as MLSPL commands, that implement classic machine learning and statistical learning tasks:

fit
: Fit and apply a machine learning model to search results. 
apply
: Apply a machine learning model that was learned using thefit
command. 
summary
: Return a summary of a machine learning model that was learned using thefit
command. 
listmodels
: Return a list of machine learning models that were learned using thefit
command. 
deletemodel
: Delete a machine learning model that was learned using thefit
command. 
sample
: Randomly sample or partition events. 
score
: Run statistical tests to validate model outcomes.
You can use these custom search commands on any Splunk platform instance on which the Splunk Machine Learning Toolkit is installed.
Download the Machine Learning Toolkit Quick Reference Guide (also available in Japanese) for a handy cheat sheet of MLSPL commands and machine learning algorithms used in the Splunk Machine Learning Toolkit.
fit
Use the fit
command to fit and apply a machine learning model to search results.
Supervised Syntax
fit <algorithm> (<option_name>=<option_value>)* (<responsefield>) from (<explanatoryfield>)+
Unsupervised Syntax
fit <algorithm> (<option_name>=<option_value>)* (from)? (<explanatoryfield>)+
The first argument, which is required, is the algorithm to use. There are a number of available algorithms, which are documented here: Algorithms
All algorithms require a list of fields to use when learning a model. For classification and regression algorithms, follow the response field with the from keyword. Subsequent fields are the fields to use when making predictions (i.e. explanatory fields). The from keyword separates the response field from the explanatory fields. When using unsupervised algorithms, there is no response field, and the from keyword is optional. In the unsupervised case, all fields listed will be treated as explanatory fields when training the model (i.e. for clustering, which fields to cluster over).
Use the as
keyword to rename the field added to search results by the model.
Use the into
keyword to store the learned model in an artifact that can later be applied to new search results with the apply
command. Not all algorithms support saved models.
Some algorithms support options that can be given as name = value arguments. For example, KMeans and PCA both support a k
option that specifies how many clusters or how many principal components to learn.
You can also configure the fit
command. See Configure the fit and apply commands.
Examples
Fit a LinearRegression model to predict errors
using _time
:
...  fit LinearRegression errors from _time
Fit a LinearRegression model to predict errors
using _time
and save it into a model named errors_over_time
:
...  fit LinearRegression errors from _time into errors_over_time
Fit a LogisticRegression model to predict a categorical response from numerical measurements:
...  fit LogisticRegression species from petal_length petal_width sepal_length sepal_width
apply
Use the apply
command to compute predictions for the current search results based on a model that was learned by the fit
command. The apply
command can be used on different search results than those used when fitting the model, but the results should have an identical list of fields.
You can also configure the apply
command. See Configure the fit and apply commands.
Syntax
apply <model_name> (as <output_field>)?
Use the as
keyword to rename the field added to search results by the model.
Examples
Apply a learned LinearRegression model, "errors_over_time":
...  apply errors_over_time
Rename the output of the model to "predicted_errors":
...  apply errors_over_time as predicted_errors
summary
Use the summary
command to return a summary of a machine learning model that was learned using the fit
command. The summary is algorithm specific. For example, the summary for the LinearRegression algorithm is a list of coefficients. The summary for the LogisticRegression algorithm is a list of coefficients for each class.
Syntax
summary <model_name>
Examples
Inspect a learned LinearRegression model "errors_over_time":
 summary errors_over_time
listmodels
Use the listmodels
command to return a list of machine learning models that were learned using the fit
command. The algorithm and arguments given when fit
was invoked are displayed for each model.
Syntax
listmodels
Examples
List all models:
 listmodels
deletemodel
Use the deletemodel
command to delete a machine learning model learned using the fit
command.
Syntax
deletemodel <model_name>
Examples
Delete the "errors_over_time" model:
 deletemodel errors_over_time
sample
Use the sample
command to randomly sample or partition events.
Sampling modes:
ratio
: A float between 0 and 1 indicating the probability as a percentage that each event has of being included in the result set. For example, a ratio of 0.01 means that events have a 1% probability of being included in the results. Useratio
when you want an approximation.count
: A number that indicates the exact number of randomlychosen events to return. If the sample count exceeds the total number of events in the search, all events are returned.proportional
: The name of a numeric field to use to determine the sampling probability of each event, which yields a biased sampling. Each event is sampled with a probability specified by this field value.
You can omit the ratio
keyword, for example use  sample ratio=0.01
or  sample 0.01
.
You can omit the count
keyword, for example use  sample count=10
or  sample 10
.
Partitioning mode:

partitions
: The number of partitions in which to randomly divide events, approximately split. Usepartitions
when you want to divide your results into groups for different purposes, such as using results for testing and training.
Additional options:

seed
: A number that specifies a random seed. Usingseed
ensures reproducible results. If unspecified, a pseudorandom value is used. 
by <field>
: Used withcount
. Specifies a field by which to split events, returning thecount
number of events for each value of the specified field. If there are more events thancount
, all events are included in the results. 
inverse
: Used withproportional
. Inverts the probability, returning samples with one minus the probability specified in the proportional field. 
fieldname
: The name of the field in which to store the partition number. Defaults topartition_number
.
This sample
command is not identical to using sampling options on the Event Sampling menu on the Search page in Splunk Web:
 Options from the Event Sampling menu perform sampling before the data is collected from indexes, at the beginning of the search pipeline.
 The
sample
command is applied after data is collected, accessing everything in the search pipeline.
Using the Event Sampling menu options is faster, but the sample
command can be used anywhere in the search command and provides several modes that are not available to the Event Sampling feature. For example, the sample
command supports partitioning, biased sampling, and the ability to retrieve an exact number of results.
Syntax
sample (ratio=<float between 0 and 1>)? (count=<positive integer>)? (proportional=<name of numeric field> (inverse)?)? (partitions=<natural number greater than 1> (fieldname=<string>)?)? (seed=<number>)? (by <split_by_field>)?
Examples
Retrieve approximately 1% of events at random:
...  sample ratio=0.01 ...  sample 0.01
Retrieve exactly 20 events at random:
...  sample count=20 ...  sample 20
Retrieve exactly 20 events at random from each host:
...  sample count=20 by host
Return each event with a probability determined by the value of "some_field":
...  sample proportional="some_field"
Partition events into 7 groups, with the chosen group returned in a field called "partition_number":
...  sample partitions=7 fieldname="partition_number"
score
The score
command runs statistical tests to validate model outcomes. Use the score command to validate models and statistical tests for any use case.
Syntax
...  score <scoring_method> (<field>  <selector>=<field>)* [as <outputfield>] [by <splitfield>] [<arg>  <param>=<value>]*  ...
Example
...  score confusion_matrix true="species" pred="predicted(species)"
The Splunk Machine Learning Toolkit includes the following classes of the score
command, each with their own sets of methods (ie. Accuracy, F1score, Ttest etc):
 Classification
 Clustering scoring
 Pairwise distances scoring
 Regression scoring
 Statistical functions (statsfunctions)
 Statistical testing (statstest)
Score commands are not customizable within the Splunk Machine Learning Toolkit.
The Splunk Machine Learning Toolkit also helps you test for model overfitting through the Kfold scoring option.
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This documentation applies to the following versions of Splunk^{®} Machine Learning Toolkit: 4.1.0, 4.2.0, 4.3.0
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