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JMP is taking Discovery online, April 16 and 18. Register today and join us for interactive sessions featuring popular presentation topics, networking, and discussions with the experts.

Practice JMP using these webinar videos and resources. We hold live Mastering JMP Zoom webinars with Q&A most Fridays at 2 pm US Eastern Time. See the list and register. Local-language live Zoom webinars occur in the UK, Western Europe and Asia. See your country jmp.com/mastering site.

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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:

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