Machine Learning Stage

Table of Contents

Configuration

The Machine Learning query pipeline stage uses a trained machine learning model to analyze a field or fields of a Request object and stores the results of analysis in a new field added to either the Request or the Context object.

In order to use the Machine Learning Stage, you must train a machine learning model. There are two different ways to train a model:

Use a Fusion job that trains a model, like Logistic Regression or Random Forest.
Train a model using Spark’s MLlib API outside of Fusion, and upload this model into Fusion’s blob store. Complete details are available in Machine Learning Models in Fusion.

When specifying field names, multiple field names are supported, in this format: field1:weight,field2:weight,field3:weight

Configuration

Use a machine learning model to generate a prediction about a query.

skip - boolean

Set to true to skip this stage.

Default: false

label - string

A unique label for this stage.

<= 255 characters

condition - string

Define a conditional script that must result in true or false. This can be used to determine if the stage should process or not.

modelId - stringrequired

The ID of the ML model stored in the Fusion blob store.

>= 1 characters

queryFeatureFieldName - string

Name of the request parameter to extract query features from (model input).

>= 1 characters

Default: q

predictionFieldName - stringrequired

Name of the field to store the prediction (model output) in the document.

>= 1 characters

defaultValue - string

Value to provide if a prediction cannot be made for a document.

failOnError - boolean

Flag to indicate if this stage should throw an exception if an error occurs while generating a prediction for a document.

Default: false

storeInContext - boolean

Flag to indicate that the prediction should be set as a context property instead of setting a field on the document.

Default: false