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103Frequently Asked Questions for The SPM® Software Suite

The Salford Predictive Modeler® software suite is a highly accurate and ultra–fast platform for developing predictive, descriptive, and analytical models from databases of any size, complexity, or organization. The SPM® software suite is in use in major organizations and by leaders in fraud detection, credit risk, insurance, direct marketing, online analytics, manufacturing, pharmaceuticals, logistics, natural resources, auditing, security, national defense, and more. Data mining technologies within the SPM® software suite span classification, regression, survival analysis, missing value analysis, and clustering/segmentation to cover all aspects of your data mining projects. The SPM® software suite's algorithms are considered to be essential in data mining circles.

The SPM® software suite's automation accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. While the analyst is always in full control we optionally anticipate the analysts' next best steps and package a complete set of results from alternative modeling strategies for easy review.

Reading MySQL tables with SPM

SPM for Windows has long had the ability to read tables in relational databases through the ODBC interface. This capability was also recently added to the command line version on Windows and it is planned on UNIX platforms (including MacOS X). The purpose of this article is to describe how to access MySQL databases specifically, but the same principles will apply to accessing data stored in other relational database systems. Probably, the only thing that will differ will be the driver used.

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Working with Scratch Directories in SPM

Like many programs, the Salford Predictive Modeler® software suite reads, writes, and otherwise manages temporary files in the course of its work. These are written to a particular directory on your computer called a "scratch directory". SPM also writes a command log to the scratch directory. The GUI version of SPM allows the location of this directory to be set as an option (with a sensible default), but non-GUI versions determine where to write temporary files by means of environment variables. Presently, SPM searches for the following environment variables and uses the value of the first one defined as its scratch directory:

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Memory Requirements for the Salford Predictive Modeler software suite

A user's license sets a limit on the amount of learn sample data that can be analyzed. The learn sample is the data used to build the model. Note that there is no limit to the number of test sample data points that may be analyzed. In other words, rows -by- columns of variable and observations used to build the model. Variable not used in the model do not count. Observations reserved for testing, or excluded for other reasons, do not count.

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Working With Date Variables

There are a variety of ways to represent dates in data files and there is standard, which can make life difficult if one is trying to use date variables in a predictive model. Two of the more common representations are the Microsoft date format (used in Excel and other Microsoft products) , which is the number of days since December 30, 1899; and the SAS date format, which is the number of days since January 1, 1960. For the sake of establishing consistency, the data access library used by SPM converts all date variables to Microsoft dates. The advantage of doing so is that one does not have to guess how dates are represented in the input dataset and Microsoft products are common; the disadvantage is that you might be confused if you are using non-Microsoft products (like SAS) to manage your data.

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How to access data in relational databases via ODBC

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SPM 6.6 (TreeNet TN 6.4) or greater supports data access to Microsoft SQL Server, Oracle, MySQL and other RDMS via ODBC interface.

Since SQL Queries cannot be entered via standard Windows ODBC dialog data source selection dialog, one has to use command line to open data directly from SQL Server.

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AutoDiscovery of Predictors in SPM

Autodiscovery leverages the stability advantages of multiple trees to rank variables for importance and thus select a subset of predictors for modeling. In SPM® v8.2 and earlier Autodiscovery runs a very simple training data only TreeNet model growing out to 200 trees. The variable importance ranking generated from this model is then used to reduce the list of all available predictors down to the top performing predictors in this background model. Autodiscovery is fast and easy, as there are no control parameters to set, but it is just a mechanism for quickly testing whether a substantial refinement in the number of predictors can improve model performance.

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