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

Learn how to

• Build and understand predictive models such as generalized regression models, k-nearest neighbors, naïve Bayes, support vector machines, decision trees, and neural networks.
• Compare and explain complex models.
• Score new data in JMP and generate score code for use in other software.

Who should attend

Data scientists, data analysts, qualitative experts, and others who want to learn and use predictive modeling using JMP Pro

Formats available	Standard Duration
Classroom:	2 days
Live Web Classroom:	4 half-day session(s)	System Requirements

Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP® Software: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.

This course addresses JMP software.

Introduction to Predictive Modeling

• How is predictive modeling different?
• Importing data and cleanup.
• Generalization and honest assessment.

Overview of Predictive Modeling Platforms

JMP platforms for predictive modeling.JMP Pro platforms for predictive modeling.

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.