Predictive Modeling Sample: Classification Trees

About This Sample

This sample demonstrates the use of the Spotfire Streaming Classification Trees operator.

The provided StreamBase modules for these operators use the IRIS Flower data - irisdat.csv. SEPALLEN, SEPALLWID, PETALLEN, PETALWID features are selected as predictors. IRISTYPE is selected as the response. Data is fed into the Matrix operator to be collected and emitted every 100 rows. Once the matrix operator has collected the required number of rows of data, the SetReady output stream sets a dynamic variable indicating the Threshold condition has been met. The first N tuples are not scored because the operator is yet to be trained. This operator uses the collected data and options (from the provided schema) as inputs.

Importing This Sample into StreamBase Studio

In StreamBase Studio, import this sample with the following steps:

  • From the top-level menu, click File>Import Samples and Community Content.

  • In the search field, type classificationtrees to narrow the list of options.

  • Select Standard classification trees from the Streaming Datascience Operators category.

  • Click Import Now.

StreamBase Studio creates a single project containing the sample files.

Running This Sample in StreamBase Studio

  1. In the Project Explorer view, double-click to open the classificationtrees application. Make sure the application is the currently active tab in the EventFlow Editor.

  2. Click the Run button. This opens the SB Test/Debug perspective and starts the application.

  3. Click on the Feed Simulations tab, click the classificationtrees.sbfs, then click the Run button to start feeding the data.

  4. These operator starts taking data from the feed simulation and emitting the results after 100 rows collected.

  5. When done, press F9 or click the Stop Running Application button.

Expected Output Stream