RobustScaler

The RobustScaler algorithm uses the scikit-learn RobustScaler algorithm to standardize data fields by scaling their median and interquartile range to 0 and 1, respectively. It is very similar to the StandardScaler algorithm, in that it helps avoid dominance of one or more fields over others in subsequent machine learning algorithms, and is practically required for some algorithms, such as KernelPCA and SVM. The main difference between StandardScaler and RobustScaler is that RobustScaler is less sensitive to outliers.

Parameters

The with_centering and with_scaling parameters specify if the fields should be standardized with respect to their median and interquartile range.

Syntax

fit RobustScaler <fields> [into <model name>] [with_centering=<true|false>] [with_scaling=<true|false>]

You can save RobustScaler models using the into keyword and apply new data later using the apply command.

... | apply scaling_model

You can inspect the statistics extracted by RobustScaler with the summary command.

... | summary scaling_model

Syntax constraints

RobustScaler does not support incremental fit.

Example

The following example uses RobustScaler on a test set.

... | fit RobustScaler *  | ...

Local availability Permalink to this section#

• Local class: RobustScaler
• Source file: Splunk_ML_Toolkit/bin/algos/RobustScaler.py (in-repo path Splunk_ML_Toolkit/bin/algos/RobustScaler.py)
• algos.conf stanza: [RobustScaler]
• Class bases: TransformerMixin, BaseAlgo

Source Permalink to this section#

Adapted from the Splunk AI Toolkit 5.6.4 documentation at /en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/algorithms-and-scoring-metrics-in-the-ai-toolkit/algorithms-in-the-ai-toolkit (section: preprocessor).