New features in IBM SPSS Modeler 18.1.1

IBM® SPSS® Modeler adds the following features in this release.

• E-Plot (Beta) node. A new beta-level E-Plot node is available on the Graphs tab of the Nodes palette. It uses a new graphing interface that is intuitive and modern, very customizable, and the data charts are interactive. Use this new node to play around with the new graphing capabilities. For details, see Using an e-plot graph.
• Spark nodes. The new Spark tab on the Nodes palette provides nodes for using Python algorithms. These new nodes are supported on Windows 64 and Mac.
  • Isotonic-AS node. A new Isotonic-AS node is available on the new Spark tab. For details, see Isotonic-AS node.
  • XGBoost-AS node. A new XGBoost-AS node is available on the new Spark tab. For details, see XGBoost-AS node.
  • K-Means-AS node. A new K-Means-AS node is available on the new Spark tab. For details, see K-Means-AS node.
• Hyper-Parameter Optimization (based on Rbfopt). A new option has been added to the One-Class SVM node (Expert tab), the XGBoost Linear node (Build Options tab), and the XGBoost Tree node (Build Options tab). The new Hyper-Parameter Optimization option automatically discovers the optimal combination of parameters so that the model will achieve the expected or lower error rate on the samples.
• Random Forest node. A new Random Forest node is available on the Python tab. For details, see Random Forest node.
• t-SNE node. A new t-Distributed Stochastic Neighbor Embedding (t-SNE) node is available on the Python tab and the Graphs tab. For details, see t-SNE node.
• Multiple data sources for the CPLEX Optimization node. Optimization experts can now import data from multiple data sources into the CPLEX Optimization node and allocate each data source to a tuple. For details, see CPLEX Optimization node and Setting options for the CPLEX Optimization node.