Speaker
Description
JMP 16 marks a substantial expansion of JMP’s capabilities. In the area of DoE, JMP 16 introduces the candidate set designer, which gives the user complete control over the possible combinations of factor settings that will be run in the experiment. The candidate set design capability is also very useful as an approach to Design for Machine Learning, where we use principles of optimal design to choose a candidate set. JMP Pro 16 also introduces Model Screening which automates fitting of a variety of machine learning models, reducing time spent in manual process of fitting and comparing various machine learning models, such as neural networks, tree based models, and Lasso regressions. JMP Pro's Text Explorer platform can now perform Sentiment Analysis, which extracts a measure of how positive or negative a document is. It also introduces Term Selection, a patented approach to identifying words and phrases that are predictive of a response. The SEM platform has seen major upgrades in the interactivity of the path diagram. I will also introduce a new platform called Model Screening which automates the process of finding the best machine learning model across many different families of models, including neural networks, regression trees, the Lasso, and much more. Along the way, we will also give pointers to other user useful capabilities that make JMP and JMP Pro 16 a powerful tool for data science and statistics.
Special/invited session
Software Session
| Keywords | DOE, Machine Learning, SEM |
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