FactorizationMachinesPredictor: Performs binary-classification or regression prediction from...

FactorizationMachinesPredictorR Documentation

Performs binary-classification or regression prediction from input vectors.

Description

The implementation of :meth:'~sagemaker.predictor.Predictor.predict' in this 'Predictor' requires a numpy “ndarray“ as input. The array should contain the same number of columns as the feature-dimension of the data used to fit the model this Predictor performs inference on. :meth:'predict()' returns a list of :class:'~sagemaker.amazon.record_pb2.Record' objects, one for each row in the input “ndarray“. The prediction is stored in the “"score"“ key of the “Record.label“ field. Please refer to the formats details described: https://docs.aws.amazon.com/sagemaker/latest/dg/fm-in-formats.html

Super classes

sagemaker.mlcore::PredictorBase -> sagemaker.mlcore::Predictor -> FactorizationMachinesPredictor

Methods

Public methods

Inherited methods

Method new()

Initialize FactorizationMachinesPredictor class

Usage
FactorizationMachinesPredictor$new(endpoint_name, sagemaker_session = NULL)
Arguments
endpoint_name

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

sagemaker_session

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: NULL). If not specified, one is created using the default AWS configuration chain.


Method clone()

The objects of this class are cloneable with this method.

Usage
FactorizationMachinesPredictor$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.


DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.