Discovering and Predicting Patterns Using Neural Network Models
Created:
Sep 24, 2020 03:19 PM
| Last Modified: Oct 25, 2023 03:11 PM
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
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