Data Processing

Download e-book for kindle: Data Mining Using SAS Enterprise Miner by Randall Matignon

By Randall Matignon

ISBN-10: 1599941988

ISBN-13: 9781599941981

The so much thorough and updated creation to information mining recommendations utilizing SAS firm Miner.

The pattern, discover, adjust, version, and examine (SEMMA) technique of SAS company Miner is a very worthy analytical software for making severe company and advertising judgements. previously, there was no unmarried, authoritative booklet that explores each node courting and development that may be a a part of the company Miner software program in regards to SEMMA layout and knowledge mining analysis.

Data Mining utilizing SAS firm Miner introduces readers to a wide selection of knowledge mining concepts and explains the aim of-and reasoning behind-every node that may be a a part of the firm Miner software program. every one bankruptcy starts off with a brief advent to the collection of data that's generated from many of the nodes in SAS company Miner v4.3, via special motives of configuration settings which are positioned inside each one node. positive factors of the booklet include:

  • The exploration of node relationships and styles utilizing info from an collection of computations, charts, and graphs generic in SAS procedures
  • A step by step method of each one node dialogue, besides an collection of illustrations that acquaint the reader with the SAS company Miner operating environment
  • Descriptive aspect of the strong ranking node and linked SAS code, which showcases the $64000 of coping with, modifying, executing, and developing custom-designed ranking code for the advantage of reasonable and entire enterprise decision-making
  • Complete assurance of the big variety of statistical suggestions that may be played utilizing the SEMMA nodes
  • An accompanying site that gives downloadable rating code, education code, and information units for extra implementation, manipulation, and interpretation in addition to SAS/IML software program programming code

This publication is a well-crafted learn advisor at the numerous tools hired to randomly pattern, partition, graph, rework, clear out, impute, change, cluster, and procedure facts in addition to interactively team and iteratively procedure information whereas acting a large choice of modeling concepts in the technique circulate of the SAS company Miner software program. info Mining utilizing SAS firm Miner is appropriate as a supplemental textual content for complicated undergraduate and graduate scholars of facts and desktop technological know-how and can be a useful, all-encompassing advisor to info mining for amateur statisticians and specialists alike.

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Example text

Therefore, high negative values of WOE correspond to high risk of loan default; high positive values correspond to low risk. After the grouping of variable values has been defined for a variable, you can assess the predictive power of the variable. The predictive power of a variable is the ability of the variable to distinguish event and nonevent observations. In this example, it is the ability to separate bad loan clients from good loan clients. You can assess the predictive power by using one the following criteria: 3 Information Value — is the weighted sum of WOE over the groups.

Observe that DEROG has relatively few observations in which DEROG > 0. Performing Variable Transformation 39 When you are finished, return to the main process flow diagram. 1 Close the distribution window when you are finished. 2 Close the Insight data table. 3 If you ran Insight without closing the node, close the Insight node (saving changes if you are prompted). Performing Variable Transformation After you have viewed the results in Insight, it will be clear that some input variables have highly skewed distributions.

To view all the effect T-Scores or estimates, select from the main menu Format Rescale Axes You can select the View Info icon ( the effect label. ) from the toolbox and then select a bar to view Now that imputation has been applied to the data set, variables DELINQ, DEROG, NINQ, CLAGE, and DEBTINC are the most important predictor variables. Data Preparation and Investigation Preliminary Investigation Recall that the Insight node gives you access to many powerful statistical graphics that are useful for preliminary investigation.

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Data Mining Using SAS Enterprise Miner by Randall Matignon

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