When the Machine Learning Toolkit is deployed on Splunk Enterprise, the Splunk platform sends anonymized usage data to Splunk Inc. ("Splunk") to help improve the MLTK in future releases. For information about how to opt in or out, and how the data is collected, stored, and governed, see Share data in Splunk Enterprise.
What data is collected
The Splunk Machine Learning Toolkit collects the following basic usage information:
Component | Description | Example |
---|---|---|
algo_name
|
Name of algorithm used in fit or apply .
|
{ "algo_name": "StandardScaler" } |
apply_time
|
Time the apply command took.
|
{ 'apply_time': 0.005 } |
app_context
|
Name of the app from which search is run. | { "app_context": "Splunk_ML_Toolkit" } |
columns
|
The number of columns being run through fit command.
|
{ "columns": 10 } |
command
|
fit or apply
|
{ "command":"apply" } |
csv_parse_time
|
CSV parse time. | { "csv_parse_time": 0.019296 } |
csv_read_time
|
CSV read time. | { "csv_read_time": 0.019296 } |
csv_render_time
|
CSV render time. | { "csv_render_time" : 0.01162 } |
example_name
|
Name of the Showcase example being run. | { 'example_name': "'Predict Server Power Consumption'" } |
experiment_id
|
ID of the fit and apply run on the Experiments page. All preprocessing steps and final fit have the same ID.
|
{ "experiment_id": "6c47bca2776d4b6cb82685461d918180" } |
fit_time
|
Amount of time it took to run the fit command.
|
{ "fit_time": 39.87447 } |
full_punct
|
The punct of the data during fit or apply .
|
{ "full_punct": [ ...s-s-s[//:::.s-]s"s/-/////.s/."sss"://:/-//@:///-."s"/.s(;sssss)s/.s(,ss)s/...s/."s-ss ] } |
handle_time
|
Time for the handler to handle the data. | { "handle_time": 0.274072 } |
num_fields
|
Total number of fields. | { "num_fields": 4 } } |
num_fields_fs
|
Number of fields that have the fs for Field Selector prefix.
|
{ "num_fields_fs": 9 } |
num_fields_PC
|
Number of fields that have the PC for preprocessed prefix.
|
{ "num_fields_PC": 70 } |
num_fields_prefixed
|
Total number of preprocessed fields. | { "num_fields_prefixed": 28 } |
num_fields_RS
|
Number of fields that have the RS for Robust Scaler prefix.
|
{ "num_fields_RS": 17 } |
num_fields_SS
|
Number of fields that have the SS for Standard Scaler prefix.
|
{ "num_fields_SS": 30 } |
num_fields_tfidf
|
Number of fields that have used term frequency-inverse document frequency preprocessing. | { "num_fields_tfidf": 9 } |
orig_sourcetype
|
The original sourcetype of the machine data. | { "orig_sourcetype" : "access_combined_wcookie" } |
params
|
Optional parameters used in fit step.
|
{ "params": "{{\"with_std\": \"true\", \"with_mean\": \"true\"}}" } |
PID
|
Process identifer associated with the command. | { "PID" : 63654 } |
pipeline_stage
|
Each preprocessing step on the Experiments page is assigned a number starting from 0. This helps determine the order of the preprocessing steps and length of the pipeline. | { "pipeline_stage": 0 } |
rows
|
The number of rows being run through fit command.
|
{ 'rows': 15627 } |
UUID
|
Universally unique identifier associated with command. This is 128-bit and used to keep each fit /apply unique.
|
{ "UUID": "7e0828e7-3059-4a43-8419-acc0e81f2f2d" } |
Model permissions in the Machine Learning Toolkit | Learn more about the Machine Learning Toolkit |
This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.4.0, 4.4.1, 4.4.2, 4.5.0, 5.0.0, 5.1.0
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