Discovering and Predicting Patterns Using Neural Network Models

See how to:

• Understand a neural network as a function of a set of derived inputs, called hidden nodes, that are nonlinear functions of the original inputs
• Interpret Neural Network diagram inputs (factors) and outputs (responses) 
• Understand terms and how they apply to building Neural Networks (nodes, activation type, activation functions)
• Understand types of activation functions  (TanH, Linear, Gaussian, Sigmoid, Identity and Radial) that transform a linear combination of the X variables at the nodes of hidden layers
• Follow sequence for building a Neural Network model 
  • Launch model (JMP vs. JMP Pro)
  • Specify # nodes in first and second layer
  • Use boosting to build a larger additive model fit on the scaled residuals of previous smaller models)
  • Specify Learning Rate (closer to 1 will run model faster, but may tend to overfit)
  • Transform covariates
  • Use Robust Fit for continuous output to minimize impact of outliers
  • Understand how JMP uses penalty methods and number of tours
  • Select Penalty Method (Squared, Absolute, No Penalty) to help avoid overfitting
  • Select number of Tours
• Case study building a neural network for a continuous response
  • Use JMP validation to avoid overfitting
  • Compare Training set and Validation set values
  • Use Model Launch to change Neural Net settings
  • Use Actual by Predicted Plot and Profiler to evaluate how predictors impact results
• Case study building a neural network for categorical response 
  • Interpret Fit Statistics, Confusion Matrix, ROC Curves and Lift Curves

Resources:

• Extrapolation Control options in Neural Net Prediction Profiler
• Understanding the Principles for Building Predictive Models video