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JMP Pro Software

JMP Pro includes powerful new capabilities for advanced analytics users who need more modern methods with greater automation to create robust predictive models


JMP Pro provides all the superior visual data access and manipulation, interactivity, comprehensive analyses, and extensible capabilities that are the hallmarks of JMP, then adds modern predictive modeling, cross-validation, exact measures of association, one-click bootstrapping and model comparison features. All of this comes in the in-memory, desktop environment familiar to current JMP users


JMP® Pro, Version 10 Features

Visual discovery


Graph Builder is the interactive drag-and-drop tool for creating a graphical analysis. This is among the many tools available for data visualization in JMP Pro.


Build predictive models that generalize well

Use cross-validation and build boosted neural network models with automatically generated SAS score code


Interactive Data Mining:-

Build boosted decision-tree models and generate SAS score code.


Compare predictions across many modeling techniques

The model comparison platform lets you compare fits across multiple fit predictions


Assign measures of precision to model predictions

Bootstrap distributions of parameters of a non-linear model.

Exact tests

Here, the Cochran-Armitage test is used to look for increasing incidence of drug effect with increasing dose. Note the difference between the asymptotic p-values and those from the exact permutation test.


Key features exclusive to JMP® Pro, Version 10:

Validation column role in many modeling platforms
Bootstrapping for most statistics in JMP reports


Fit Model platform

Partial least squares implementation allows specification of model terms
Stepwise regression uses train, validate and test methodology


Partition platform

Bootstrap forest and boosted tree techniques
Uses train, validate and test methodology


Model Comparison

Compares the fit of different models
Provides measures of fit, ROC, diagnostic plots and profilers


Contingency platform

Exact measures of association


Oneway platform

Nonparametric exact tests


Neural platform

Automated handling of missing data
Automatic selection of the number of hidden units using boosting
Automated transformation of input variables
Outlier-resistant loss function
Ability to fit both one- and two-layer neural nets
Ability to choose from three activation functions
Ability to save transformed covariates
Ability to save randomly generated cross-validation columns
Uses train, validate and test methodology


PLS platform

Choice of k-fold or Holdback validation methods


PCA Platform

Variable clustering option for predictor variable reduction prior to modeling

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