New in JMP 13 and JMP Pro 13 (PDF)
As the advanced analytics version of our software, JMP Pro contains everything users know and love about JMP – and more. With the release of JMP Pro 13, users will enjoy new capabilities, new improvements to the predictive modeling workflow and performance improvements across most of the JMP Pro platforms.
Introduced in JMP 13, the Text Explorer is a platform for dealing with unstructured text data. While JMP 13 provides methods for basic word and phrase extraction, JMP Pro adds significant capabilities for multivariate analysis and dimension reduction, enabling users to incorporate text data into their predictive modeling tasks.
JMP Pro can take the types of text data that almost everyone has—repair logs, free-text surveys, long-form description fields and open comments, to name a few—and through the use of tailor-made analytics, convert that text data into numerical data that can be used directly by the JMP Pro modeling platforms. Now you can take the information within your unstructured data and use it to enrich your favorite JMP Pro model, be that a Bootstrap Forest, Neural Net or Generalized Regression model.
By complementing the predictors you already have with the text data, you can build models with better external validity. You are already going through the pain of storing all of this unstructured data in your database—why not put it to work for you?
The goal of JMP Pro Generalized Regression is to satisfy all of your modeling needs. Generalized Regression is the JMP modern approach to Generalized Linear Models and variable selection, as well as a world-class tool for analyzing DOEs and observational data – even life/survival data – all in one place.
JMP 13 marks Generalized Regression’s third release, and the tool continues its evolution with many additions and new features:
In previous versions of JMP, simulation could only be used in those reliability settings where a failure meant end-of-life for the system. However, a complex, expensive system built from many serviceable components—for example, an airplane engine—is usually repaired if possible, rather than discarded when it fails. Repairable Systems Simulation (RSS) in JMP Pro 13, analysis of repairable systems is now possible, enabling you to answer questions such as:
Since the RSS platform in JMP Pro 13 uses the same interface as the Reliability Block Diagram platform, reliability engineers won’t have to learn a new interface—they can get right to work building models.
Statisticians and analysts use simulations to evaluate new statistical methods, estimate power for nonstandard statistical tests and perform parametric bootstrapping. In JMP 13, access to general simulation capability is significantly broadened, as users no longer need to write custom JSL to perform the simulation and analyze the results. This capability is available from within most platforms having Auto Recalc or Bootstrap as red triangle menu options.
DOE is another area in which this simulation is useful. In designed experiments, the responses are not always distributed normally or approximately normally. System tests in particular can generate data that is count- or pass/fail-based. The Custom Designer in JMP 13 can now simulate realistic response data for these experiments, and JMP Pro users can use this simulated output along with the JMP Pro general simulation capabilities, to estimate the experimental power of the design.
Finally, Bagging (Bootstrap Aggregating) has been added to the Profiler in JMP Pro, making it possible for users to construct prediction intervals in settings where prediction interval formulas do not exist.Creating a validation column was largely a manual process that required multiple clicks to divide a data set into training, validation and test sets. If you required more than a simple random sample of the data for splitting, you had to use an add-in or other techniques to generate the best splits.
Now you can split data based on the problem, using one of several included algorithms. Also, if you are in an analysis platform, clicking on the validation column role without selecting a column will prompt you to choose a validation column, or create one directly from the platform. This keeps you in the analysis flow and lets you cross-validate models easier, in fewer clicks.
Is your current design powerful enough to be useful to you? In many non-traditional experimental settings, this is difficult to answer. The JMP Pro 13 simulation features give you peace of mind, by letting you answer these questions before implementing the design. The validation column creation utility lets you automatically build a validation column, stratified if desired.
Choice models can help companies decide which products and features are most important to customers, and how much customers are willing to pay for certain features.
A choice model that treats all customers as if they were the same will produce estimates that average the preferences across all individuals. If, as is often the case, the individuals actually have significant differences in preferences, the “optimal” product as determined by the model may satisfy this “average” customer—who does not actually exist—but possibly appeal to none of the customers that do exist.
Fortunately, JMP 13 Pro supports Hierarchical Bayes, which can properly model these differences in preferences. Use it to get better results and higher quality models from your Choice designs, maximizing the information gained from your limited run budget.
Association analysis (often called Market Basket Analysis) is the identification of items that occur together in a given event, record or transaction.
Here are some examples of association rules:
Understanding these associations can inform decision-making in a variety of contexts such as marketing, healthcare, and product reliability.
With the JMP Pro Association Analysis platform, you can also use Singular Value Decomposition (SVD), a dimension-reduction technique, to group similar transactions. The singular vectors can then be used in predictive modeling platforms.
Gaussian Process models are used to model the relationship between a continuous response and one or more predictors. In previous versions of JMP Pro, running these models was time-consuming for large data sets, and—regardless of table size—impossible for models with categorical factors. In JMP Pro 13, both of these issues have been addressed, greatly extending the reach of Gaussian process models.
In JMP 13, the Mixed Model personality of Fit Model offers several new covariance structures (Unequal Variances, Exchangeable, Antedependent, Toeplitz), extending its applicability to a variety of new contexts.
JMP Pro 13 gives you more control over the Partition platform: model tuning tables let you run models over a grid of parameter values, while the randomization in Stochastic Gradient Boosting option for boosted trees helps protect against overfitting. The Naïve Bayes classifier is also available.
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