JMP Pro Users Group

Presentation given by JMP Systems Engineer Scott Allen on his Predictor Selection Assistant Add-In. The Predictor Selection Assistant (PSA) is an add-in that leverages the Predictor Screening and Bootstrap Forest platforms to provide additional detail on the likely strength of predictors. The add-in classifies a predictor as Strong, Medium, or Weak in relation to a given output, with further statistical comparisons available to inform whether a predictor should be retained or removed for further investigation. Use the Predictor Selection Assistant to help you identify critical parameters in root cause investigations, prioritize factors for designed experiments, or select features for modeling and machine-learning platforms in JMP Pro. To learn more about this add-in or to download it, Click Here. ... View more

During our March meeting, JMP's Haley Yaremych presented on her Moderation and Mediation Add-In for JMP Pro. Some of the feedback we received was that it was unclear how the add-in could help in a non-social science setting. Haley took that feedback and developed a use case for the add-in in the physical sciences.  In a new blog post, she uses chemical purity data to demonstrate how to fit and visualize a model with an interaction using the add-in. The add-in automates model specification and fitting, and produces detailed numeric and graphical output about the interaction. Click Here to read Haley's post. Click Here to view Haley's original presentation. Click Here to download the add-in. ... View more

Tuning hyperparameters is crucial for optimizing machine learning models, but the process can be computationally expensive and complex. Traditional grid, random search, or even Bayesian optimization methods often miss critical areas of the hyperparameter space, leading to suboptimal models. In this talk, we show a JMP add-in we have developed that uses space-filling DOE to more efficiently approach the hyperparameter tunning challenge. The use of space-filling DOE ensures that hyperparameter combinations are sampled more evenly across the entire parameter space, thus reducing the number of required evaluations while increasing the likelihood of finding optimal settings. This talk also highlights the improved integration with Python found in JMP 18 and how leveraging capabilities like DOE inside JMP can be beneficial to data scientists. This talk combines advanced statistical techniques with practical, accessible tools to enhance model performance in diverse applications. Click Here to view presentation. Click Here to download the Torch Companion Add-In. ... View more

Presentation from JMP Analytics Software Tester Haley Yaremych on the Moderation & Mediation for JMP Pro Add-In. Often as we are trying to gain insights from our data, understanding that two variables are related is not enough. We need to dig deeper and ask questions like: under what circumstances are they related? For whom are they related, why are they related, and how? Moderation, mediation, and moderated mediation allow us to answer exactly these types of questions. The Moderation and Mediation for JMP Pro add-in allows users to easily specify, fit, and visually probe interactions in three popular models. To download the add-in, Click Here. You may also find it helpful to read the blog post that Haley made: Who, what, why, and how? Tools for modeling and visualizing moderation and moderated mediation in JMP Pro. ... View more

Presentation from Senior JMP Systems Engineer Ross Metusalem on Sentiment Analysis. Sentiment analysis can be used to quantify the positive or negative sentiment in unstructured text data. This can be of particular use in manufacturing to analyze text data coming from things like customer reviews, warranty claims, call center transcripts, machine maintenance logs, etc. In addition to this presentation, you may find the below resources helpful. Organizing Text for Analysis Expanding Our Text Mining Toolkit: Sentiment Analysis and Term Selection in JMP Pro 16 ... View more