| Segment | Description | Who's On Air |
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Welcome |
The Monologue | |
| Tip of the Day |
In today’s Tip of the Day, Pete and Mary explore one of JMP’s most powerful tools, the Prediction Profiler. The Prediction Profiler in JMP gives you a wealth of information about your model. They’ll show you how to use the Prediction Profiler to understand statistical models and how it changes your model as you change settings of individual factors. Bonus tip: Pete and Mary will show you how to use the handy “Lock Factor Settings” to freeze a particular factor while you manipulate other factors.
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@Peter_Hersh |
| Featured Program |
Research Statistician Developer Elizabeth Claassen kicks off JMP On Air’s Predictive Modeling Day with an introduction to machine learning. Machine Learning, according to Elizabeth is the latest, current buzz-phrase meant to encompass the computer algorithms used to make decisions, predictions, or classification based on data. During this presentation, Elizabeth introduces the different types of Machine Learning algorithms – including supervised and unsupervised machine learning – and the possible trade-off of accuracy vs. interpretability. After discussing the most impactful aspects of machine learning, Elizabeth pulls up JMP to show you how well the software performs as a machine learning platform. Spoiler Alert: JMP can do machine learning very well.
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@eclaassen |
| Featured Program |
Exploring Outliers in Your Data SAS Analytical Training Consultant and JMP Statistical Trainer Ledi Trutna shares why is it critical to explore and manage outliers in your data. She’ll discuss what you need to know about these data points, including how and why you should examine them while cleaning up your data and how to use JMP to handle them easily. Along, the way you’ll also learn how to use the “Explore Outliers” platform, which allows you to be much more precise with how you define outliers. After Ledi shows you how to identify your outliers, she discusses what actions you can take, specifically if you should Welcome, Reject or Accommodate the outlier.
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@ledi_trutna |
| Featured Program |
In this episode of Which Model When, Systems Engineer Mary Loveless goes house hunting; more specifically, house hunting in Cincinnati, Ohio. Together, Mary and Academic Manager Ruth Hummel use real estate data to determine how to predict a selling price for homes in The Queen City. First, Mary encourages you not to forget an often-overlooked step: running summary statistics to familiarize yourself with your data. (Mary is looking for, in particular, things like number of missing data, max, min and averages to help her build her model.) Next Mary and Ruth will tell us about different types of predictive models, and how to choose among them when working with your own data. Finally, the ladies show you how JMP’s Prediction Profiler can easily adjust your various features to show the effect it would have on other factors. But, I’m not going to give everything away. You’ll have to tune in to find out what home Mary ultimately chooses.
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@ruthhummel |
| Featured Program |
Quick Start: Predictive Models - Tips & Tricks to Build Models Quickly - Predictive Modeling Sr. Systems Engineer Kemal Oflus gives you helpful hints for building your models more effectively. In this presentation, Kemal offers guidance on a number of critical predictive modeling considerations, from improving your data cleanup and visualization to identifying the best predictive modeling option for your needs. You’ll also learn why Kemel recommends, before doing any model building, that you first visualize your data to better understand what’s in there. He also shows you how to use JMP Graph Builder to see if there’s anything hidden in your data that might help or impede your model building.
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@kemal_oflus |
| Featured Program |
Machine Learning Modeling Tips In the final session of JMP On Air’s Predictive Modeling Day, Director of JMP Research and Development Chris Gotwalt gives us his machine learning tips and shows us how he builds successful models in JMP. After a brief overview that compares classical modeling against machine learning modeling, Chris discusses how he sets up data for successful modeling, how he uses visualizations to identify good models and diagnose problems, and how to use the ‘Partition Trick’ to solve rare event classification problems.
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@chris_gotwalt1 |
| Closing | The Last 5 | @julian |