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JMP® Education

ADVANCED TOPICS AND JMP PRO

JMP® Pro: Predictive Modeling

This course covers the skills required to develop, assess, tune, compare, and score predictive models using JMP Pro software.

This course teaches you how to build and understand predictive models using machine learning techniques such as generalized regression models, k-nearest neighbors, naïve Bayes, support vector machines, decision trees, and neural networks. You will also learn how to validate predictive models using cross-validation, holdback validation, and information-theoretic criteria.

Duration: 4 half-day sessions

Learn how to:
  • Develop, compare and explain complex models.
  • Use the partition platform for predictive modeling including bagging, bootstrap forest, boosted trees.
  • Use neural networks for predictive modeling including k-Fold cross validation, multi-layer neural networks, and boosting.
  • Tune predictive models.
  • Score new data in JMP and generate score code for use in other software.
  • Deploy predictive models to production.
Introduction to Predictive Modeling
  • How is predictive modeling different?
  • Importing data and cleanup.
  • Generalization and honest assessment.
Overview of Predictive Modeling Platforms
    Predictive Modeling with the Partition Platform
    • Recursive partitioning.
    • Bagging.
    • Bootstrap forest.
    • Boosted tree models.
    • Tuning,
    Predictive Modeling with Neural Networks
    • Fitting a neural network model.
    • Neural network architectures.
    • k-Fold cross validation.
    • Boosted neural networks.
    • Tuning.
    Choosing and Deploying Predictive Models
    • Model comparisons.
    • Ensemble models.
    • Formula depot.
    Course Dates

    This course is not available at this time. Please check back at a later date.

    Don’t see a course you want listed on the dates that you need? Contact us at training@jmp.com.

     

    Contact JMP Education