Model validation, a method of determining if a predictive model is generalizable to new data, is critical when using models to help make decisions. Data can be partitioned into sets before modeling to avoid overfitting. Part of the original data is used to estimate parameters and the rest of the data is used to tune or evaluate the parameters.

JMP Pro incorporates validation into some of its models and allows away to interactively create data partitions in others.

See how to:

• Understand the value and importance of validating your models.
• Understand how test, training and validation sets work.
• Use different JMP Pro modeling techniques, including advanced options like PyTorch for neural networks and XGBoost (eXtreme Gradient Boosting)
• Screen models.
• Deploy models to put into action the insights they provide.

This webinar covers: validation techniques, decision trees, model scoring, and model selection.