Understanding the ML algorithm used by Amazon QuickSight - Amazon QuickSight

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Understanding the ML algorithm used by Amazon QuickSight

You don't need any technical experience in machine learning to use the ML-powered features in Amazon QuickSight. This section dives into the technical aspects of the algorithm, for those who want the details about how it works. This information isn't required reading to use the features.

Amazon QuickSight uses a built-in version of the Random Cut Forest (RCF) algorithm. The following sections explain what that means and how it is used in Amazon QuickSight.

First, let's look at some of the terminology involved:

  • Anomaly – Something that is characterized by its difference from the majority of the other things in the same sample. Also known as an outlier, an exception, a deviation, and so on.

  • Data point – A discrete unit—or simply put, a row—in a dataset. However, a row can have multiple data points if you use a measure over different dimensions.

  • Decision Tree – A way of visualizing the decision process of the algorithm that evaluates patterns in the data.

  • Forecast – A prediction of future behavior based on current and past behavior.

  • Model – A mathematical representation of the algorithm or what the algorithm learns.

  • Seasonality – The repeating patterns of behavior that occur cyclically in time series data.

  • Time series – An ordered set of date or time data in one field or column.