A photograph of cheese with holes to symbolize data with gaps and holes that can be processed by the SageMaker Factorization Machines algorithm

Factorization Machines Algorithm

The Factorization Machines Algorithm has two modes: Classification and Regression. Classification is a binary method that returns either one or zero and a label which is a number. The Regression mode returns the predicted value. Factorization Machines are a good choice for high dimensional, sparse datasets. Common uses are web page click prediction and item recommendation.

For dense continuous data Linear Learner may be a better choice. However if you have sparse data with gaps and holes Factorisation Machines Algorithm may produce better results.

Attributes

Problem attributeDescription
Data types and formatTabular
Learning paradigm or domainSupervised Learning
Problem typeBinary classification, Regression
Use case examplesPredict a numeric/continuous value; Predict if an item belongs to a category

Training

For Factorization Machines Algorithm the training data has to be in x-recordio-protobuf format.

Model artifacts and inference

DescriptionArtifacts
Learning paradigmSupervised Learning
Request formatJSON, x-recordio-protobuf

Processing environment

Training can be done on CPU and GPU instances and distributed processing is supported. CPU instances are recommended for sparse data and GPU for dense data.

Video

AWS Partner Webinar: Object2Vec on Amazon SageMaker

This is a 50.57 minutes video from AWS. It includes Factorization Machines and Object2Vec.

Credits

Cheese Photo by NastyaSensei from Pexels

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