Optimizing Your Tuning Parameters to Quickly Achieve the Superior Model Accuracy Expected from Expert Data Scientists and Statisticians Using Minitab's Salford Predictive Modeler (ADDED FEE) — Professional Development Computer Technology Workshop
Advanced modelers must tune their machine learning algorithms to create the best-performing model. For some, "best" means best performance, for others, "best" means the best balance of performance and simplicity, while for others, "best" means something else entirely. Model tuning is often a difficult and time-consuming process involving the generation of many related models. Minitab®'s SPM® automates this process, by automatically creating a series of predictive models using a systematic variation of model parameters. The underlying predictive model algorithm could be CART®, TreeNet®, RandomForests® or MARS®, but the final results are a series of models displayed so that you can easily compare them. This permits rapid perfection of model parameters and serves as a guide to model development while automatically completing these tasks in the same way leading data scientists structure their work. You can use automates to run hundreds or thousands of related models as you look for best performance or the best balance of performance and simplicity. Salford Systems SPM provides 70+ AUTOMATES. A few examples will be discussed in this presentation: Automate Priors and Fraud Detection, Automate Missing Value Indicators and Market Research, Automate Target and Engineering Applications, Automate Sample and Web Advertising, and Automate Shave and Manufacturing.
Instructor(s): Cheryl Pammer, Minitab, Inc.; Mikhail Golovnya, Minitab, Inc.; Charles Harrison, Minitab, Inc.