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
This template features a typical analytical workflow for building predictive classification data mining models with TIBCO Statistica®. In this template the user can simply change the input data source and run the whole modelling process on the new data with one click.
This is TIBCO Statistica® template guiding users through the process of data preparation steps. It is meant to be a quick start template allowing users to build their own data preparation process quicker. User will go through the workflow, set, connect and use various nodes in a sequence in order to prepare and clean the data for further analyses.
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.