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.
The SageMaker DeepAR Forecasting Algorithm forecasts how the target time series will evolve based on past performance. AR, which stands for AutoRegression, is a statistical method that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. The forecast is a one dimensional time…
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…
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…
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…
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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