Gradient Boosting Machine Analysis Template for TIBCO Spotfire®

This template is used to create a GBM machine learning model to understand the effects of predictor variables on a single response. 

Compatible Products

TIBCO Spotfire®

Provider

TIBCO Software

Supported Versions

Tested with Spotfire 7.7.0.39, TERR 4.2.0.35

License

TIBCO Component Exchange License

Overview

This template is used to create a GBM machine learning model to understand the effects of predictor variables on a single response.  Examples of business problems that can be addressed include understanding causes of financial fraud, product quality problems, equipment failures, customer behavior, fuel efficiency, missing luggage and many others. 

You can import your own data into this template, configure and perform the analysis, evaluate the model quality and visualize the results.  It is designed for use by a business analyst or citizen data scientist.  No specialized knowlege of statistics, data science or programming is assumed.

The Gradient Boosting Machine (GBM) machine learning model used is an ensemble decision tree method.  It is implemented using the CRAN gbm package within the Spotfire interface.  For more information on machine learning algorithms and use cases visit the Machine Learning Wiki page.

License Details

Release(s)

Release v1.0

Published: November 2016

Initial Release

Reviews (12)
5
mdutta 8:57am 02/13/2018

One of my favourite templates! Excellent job.

5
jeyu 12:12pm 07/10/2017

Great prediction model to show how easy a stat function to be used in spotfire 

5
amartens 2:56am 07/06/2017

Greate template for applying Machine Learning to your own data set. Easy to use and well documented. Thanks!

5
rtanuwaj 9:46pm 07/05/2017

Good sample for supervised model, can be used for financial industry to create behaviour & collection scoring.

5
ijames 9:10pm 07/04/2017

Excellent and easy way to start an analysis looking to predict a numeric or binary response when you’re not sure what predictors you should be using. Tells you which are the best predictors and any relationships between them.
Well explained and easy for a novice to use and understand.

Pages

Gradient Boosting Machine Template for Spotfire - Reference page

 

The Gradient Boosting Machine Template for Spotfire is used to create a GBM machine learning model* to understand the effects of predictor variables on a single response.  Examples of business problems that can be addressed include understanding causes of financial fraud, product quality problems, equipment failures, customer behavior, fuel efficiency, missing luggage and many others. 

You can import your own data into this template, configure and perform the analysis, evaluate the model quality and visualize the results.  It is designed for use by a business analyst or citizen data scientist.  No specialized knowlege of statistics, data science or programming is assumed.

The GBM implementation in this template has the following features:

  • Accepts one numeric or binary (O/1) response (output) variable.  Some common binary responses are good / bad or pass / fail classifications.
  • Accepts numeric or string predictor (input) variables
  • Visualize nonlinear response-predictor relationships
  • Visualize predictor interactions
  • Model can include a large number - hundreds or thousands - of predictor variables.
  • Results rank the most important predictor variables needed to accurately model the response.  Less relevant or redundant predictors have lower variable importance ranks and can often be ignored.
  • High prediction accuracy
  • Handles missing data
  • Usually not necessary to filter outliers or transform data

* The Gradient Boosting Machines (GBM) machine learning model used is an ensemble decision tree method.  It is implemented using theCRAN gbm package within the Spotfire interface.  For more information on machine learning algorithms and use cases, visit the TIBCO Community Machine Learning Wiki page 

 

          Configuring and Evaluating the Model

 

           GBM Results:  Individual Predictor Effects on the left and Predictor Interaction Effects on the right 

View the Wiki Page