Analytics Random Cut Forest (RCF)

Analytics Random Cut Forest (RCF) is an unsupervised machine learning algorithm that is used for anomaly detection. In RCF, each tree is constructed by randomly selecting a subset of features and then randomly selecting a subset of data points from the training dataset. This process helps to ensure that the trees are more diverse and less correlated, which makes them more effective at detecting anomalies.

  • One example of a use case for RCF is anomaly detection in time series data.

  • Another example of a use case for RCF is fraud detection. Here are some additional details about RCF:

  • It is a fast and efficient algorithm that can be used to process large datasets.

  • It is relatively easy to interpret and understand.

  • It is effective at detecting both point anomalies and contextual anomalies.

  • It can be used to detect anomalies in a variety of data types, including time series data, sensor data, and financial data.