Predictive modeling encompasses a range of techniques for using historical data to predict future outcomes, and it has applications in nearly all quantitative disciplines. The JMP Student Edition has robust predictive modeling capabilities, including a range of algorithms for classification and regression, automated model cross-validation, and model comparison and deployment. And it’s no-code interface makes advanced predictive modeling approachable to students of varied backgrounds.

This webinar demonstrates tools and tips for teaching predictive modeling with the JMP Student Edition. Topics will include:

• Fitting a variety of model types (e.g., decision trees, neural nets)

• Visualizations for interpreting models

• Model cross-validation and comparison

• The Torch Deep Learning add-in for deep neural nets (including image classification)

• JMP teaching tips and resources