This template detects anomalous data points in a dataset using an autoencoder algorithm. It features automated machine learning to facilitate use by business analysts and citizen data scientists. The Time Series release of the template includes time series analysis and clustering of anomalies
Geospatial data can come in several forms, for example locations of specific points, or polygons that define areas of interest. When both point and polygon data are present, it is often useful to identify the enclosing polygon for each point, so information from both tables can be merged
This data function clusters objects together based on similarities between the objects in each cluster. After identifying clusters, the function then ranks the input variables according to their influence on cluster formation.
Random Forest is an ensemble tree machine-learning algorithm. This template employs supervised learning to determine variable importance and make predictions. It features automated machine learning to facilitate use by business analysts and citizen data scientists.