We recently had a question about running a model using GPS, and wanted to share the answer in case anyone else has the same issue.
Have you checked out Salford Systems new responsive website? According to Wikipedia,
Responsive web design (RWD) is a web design approach aimed at crafting sites to provide an optimal viewing experience—easy reading and navigation with a minimum of resizing, panning, and scrolling—across a wide range of devices (from mobile phones to desktop computer monitors). With this in mind, we have undergone a website makeover; making it easier for you to use Salford's data mining resources from your mobile and/or tablet.
This quick note is geared to help those who desire/require the use of multithreading capabilities for their predictive modeling projects. First, it should be noted that not all of the SPM components are completely multithreaded. TreeNet, Generalized PathSeeker (GPS), and pipeline tools ISLE and RuleLearner are the modules which have the most parallelization.
The recent launch of the Salford Predictive Modeler software suite v7.0, the latest version of Salford Systems’ data mining software package, spurred enormous interest in the data science community, especially after its showcase webinar series: “The Evolution of Regression Modeling.” The series’ instructors, Salford Systems’ CEO and Founder Dr. Dan Steinberg and Senior Scientist Mikhail Golovnya, will continue this showcase of the software’s forefront predictive methodology at the Joint Statistical Meetings in Montreal.
For the last decade Salford Systems has hosted computer training workshops at JSM to educate the leading statisticians, analysts, data scientists, and researchers on its flagship products CART® decision trees, MARS® nonlinear regression, TreeNet® stochastic gradient boosting, and Random Forests®. In conjunction with the software suite’s new model compression techniques and hybrid modeling capabilities, Salford Systems will present yet another new algorithm that has been added to the Salford toolkit, Generalized PathSeeker (GPS). This technology includes methods like LASSO, Ridge, and regularized regression.
Familiarize yourself with CART decision tree technology in this beginner's tutorial using a telecommunications example dataset from the 1990s. By the end of this tutorial you should feel comfortable using CART on your own with sample or real-world data.
This guide is for data mining practitioners or data scientists with experience using CART Classification and Regression Trees. Walk yourself through the slideshare for a more in-depth understanding of how CART decision trees can be implemented in today's data mining applications.
This blog post is extracted from one of Salford Systems' video tutorial lectures offered by Dan Steinberg. Take some time out of your day to improve your knowldege of CART.
The explosion of interest in predictive analytics and in particular sophisticated predictive analytics has led to renewed interest in the topic of how to plan for an predictive analytics project. The topic is hardly new but anyone involved in planning for, or conducting a serious analysis project needs to cognizant of the fundamentals. Here we abstract from insights and ideas from two classic discussions, the 1996 paper by Fayyad, Piatetsky-Shapiro, and Smyth ‘From Data Mining to Knowledge Discovery in Databases,” and the 1999 “Cross-Industry Standard Process (CRISP) for Data Mining” developed by analytical practitioners at Daimler-Chrysler, ISL (later acquired by SPSS and now part of IBM) and NCR (Teradata). While these first-generation discussions clearly date from an earlier technological era the principle ideas are still relevant and are being rediscovered and translated into modern terminology daily. Here we offer a summary of the essentials viewed from our perspective of 20 years of experience in the world of data mining and predictive analytics.
In SPM v7.0 (Salford Systems' latest product release) one of the new features available in the TreeNet component (Ultra version) is the ability to build Random Forests models. By building the model in the TreeNet engine, you're able to take advantage of the powerful technology offered by gradient-boosted trees.