Process Flow Diagram Logic

Modeling Nodes

Use the Regression node to fit both linear and logistic regression models to your data. You can use continuous, ordinal, and binary target variables. You can use both continuous and discrete variables as inputs. The node supports the stepwise, forward, and backward selection methods. A point-and-click interaction builder enables you to create higher-order modeling terms.
Use the Tree node to fit decision tree models to your data. The implementation includes features that are found in a variety of popular decision tree algorithms (for example, CHAID, CART, C4.5, and C5.0.) The Tree node supports both automatic and interactive training. When you run the Tree node in automatic mode, it automatically ranks the input variables based on the strength of their contribution to the tree. This ranking may be used to select variables for use in subsequent modeling. You may override any automatic step with the option to define a splitting rule and delete explicit nodes or subtrees. Interactive training enables you to explore and evaluate a large set of trees as you develop them.
Use the Neural Network node to construct, train, and validate multilayer feedforward neural networks. By default, the Neural Network node constructs multilayer feedforward networks that have one hidden layer that contains three neurons. In general, each input is fully connected to the first hidden layer, each hidden layer is fully connected to the next hidden layer, and the last hidden layer is fully connected to the output. The Neural Network node supports many variations of this general form.
Use the Princomp/Dmneural node to fit an additive nonlinear model that uses the bucketed principal components as inputs to predict a binary or an interval target variable. The Princomp/Dmneural node also performs a principal components analysis and passes the scored principal components to the successor nodes.
Use the User Defined Model node to generate assessment statistics using predicted values from a model that you built with the SAS Code node (for example, a logistic model using the SAS/STAT LOGISTIC procedure) or the Variable Selection node. The predicted values can also be saved to a SAS data set and then imported into the process flow with the Input Data Source node.
Use the Ensemble node to create a new model by averaging the posterior probabilities (for class targets) or the predicted values (for interval targets) from multiple models. The new model is then used to score new data. One common ensemble approach is to resample the training data and fit a separate model for each sample. The component models are then integrated by the Ensemble node to form a potentially stronger solution.
The Memory-Based Reasoning experimental node uses a k-nearest neighbor algorithm to categorize or predict observations.
The Two Stage Model node computes a two-stage model to predict a class target and an interval target. The interval target variable is usually the value that is associated with a level of the class target variable.

Note: These modeling nodes use a directory table facility, called the Model Manager, in which you can store and assess models on demand. The modeling nodes also enable you to modify the target profile(s) for a target variable. [cautionend]

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