Analyze your Big Data FAST with the use of this accelerator. Gain insights into your historical data and act in real time on the current streams of data in conjunction with historical analysis to make crucial decisions when it matters.
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 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.
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.
Gradient boosting is an ensemble-decision-tree, machine learning data function that’s useful to identify variables that best predict some outcome and build highly accurate predictive models. For example, a retailer might use a gradient boosting algorithm to determine the propensity of customers to buy a product based on their buying histories.