Summary: 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: 14 hours of content.
Modalities:
- live online with instructor -- This course is available periodically in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
- through a third-party training vendor -- Any course in our JMP Curriculum could be taught by a licensed training vendor, including through the training department at your own company. Contact your JMP representative to learn more.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Learning Objectives:
- 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.
Course Outline:
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.