Understanding the ML algorithm used by Insights

You don't need any technical experience in machine learning to use the ML-powered features in Insights. 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.

Insights 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 Insights.

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

  • 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.


  • What RCF is and what it does

  • How RCF is applied to detect anomalies

  • How RCF is applied to generate forecasts

  • References for machine learning and RCF