A photograph of a pasta machine making spaghetti symbolizing how SageMaker unsupervised learning algorithms process tabular data

SageMaker supervised algorithms

There are five SageMaker supervised algorithms for tabular data. DeepAR Forecasting uses Deep Learning for financial forecasting. Linear Learner is good for regression problems. Factorization Machines can be used for the same purpose, but can handle data with gaps and holes better. K-Nearest Neighbor is good at categorising data. XGBoost can predict if an item belongs to a category.

These revision notes are part of subdomain 3.2 Select the appropriate model(s) for a given machine learning problem of the exam syllabus.

A photograph of hands on a table to symbolize the SageMaker K-Nearest Neighbor algorithm
Modeling (Domain 3)

K-Nearest Neighbors Algorithm

The K-Nearest Neighbors Algorithm is used to place data into a category for example in recommendation applications used for recommending products on Amazon, articles on Medium, movies on Netflix, or videos on YouTube. It returns results based on the nearest training data points to the sample datapoint, also called nearest neighbors.  The K-Nearest Neighbors algorithm…

A photograph of a washing line with pegs to symbolize the SageMaker Linear Learner algorithm
Modeling (Domain 3)

Linear Learner Algorithm

Linear Learner Algorithm is a Supervised Learning algorithm that can be used to solve three types of problems: Binary classification; Multi-class classification; and Regression. The algorithm is trained with lists of data comprising a high dimensional vector x and a label y to learn the equation of the line. The Linear Learner Algorithm uses Stochastic…

A photograph of boys playing Rugby and being lifted up in the air to symbolize the SageMaker XGBoost algorithm
Modeling (Domain 3)

XGBoost Algorithm

XGBoost Algorithm stands for eXtreme Gradient Boosting. XGBoost uses ensemble learning, which is also called boosting. The results of multiple models are grouped together to produce a better fit to the training data. Each decision tree model is added using the prediction errors of previous models to improve the fit to the training data. XGBoost…


SageMaker has five built-in algorithms for tabular data that use Supervised Learning. The use cases overlap, but each algorithm has it’s own features that may make it an appropriate choice for a problem or not.


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