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
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.
Two-dimensional binning with hexagonally-arranged bins of (x,y) inputs. Useful in Spotfire for simplifying an (x,y) scatter plot with a large number of points to produce a sampling density plot. Also if an optional 3rd value column is included, a Spotfire scatter plot visualization can be constructed for the mean value across cells.