By Randall Matignon
Info Mining utilizing SASR company Miner introduces the reader to a large choice of knowledge mining innovations in SASR firm Miner. This first-of-a-kind booklet explains the aim of - and reasoning at the back of - each node that could be a a part of firm Miner in regards to SEMMA layout and information mining research. every one bankruptcy starts off with a brief advent to the collection of information which are generated from a few of the firm Miner nodes, through specified causes of configuration settings which are positioned inside each one node. the result of the author's meticulous presentation is a good crafted research consultant at the quite a few tools that one employs to either randomly pattern and partition information in the approach stream of SASR firm Miner.
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Facts Mining utilizing SASR company Miner introduces the reader to a large choice of information mining concepts in SASR company Miner. This first-of-a-kind ebook explains the aim of - and reasoning at the back of - each node that may be a a part of firm Miner with reference to SEMMA layout and information mining research.
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Extra info for Data Mining Using SAS Enterprise Miner
Pop-up menu item with the following Define Target Values window appearing. The kvindow is identical to the default settings where you may enter the same entries from the Interval subtab at the Enterprise Miner project level. B), default, the minimum and maximum values are set at the range of values in the active data set, and the number of points is automatically set to 2. The lMinimum entry field sets the lower bound to the target values. Conversely, the Maximum entry field sets the upper bound to the target values.
Therefore, select the Maximize profit with costs option in order to maximize profit based on the associated fixed costs, since we will be entering the separate costs for each decision. The Maximize profit with costs option is the only available option that will allow you to assign a cost variable or a constant cost in defining revenue amounts for each decision of the profit matrix. Adding Additional Decision Levels For a binary-valued target variable, it is important to understand that we are not limited to the default 2 x 2 matrix of two r o b s for the two class levels and tivo columns for the two separate decision levels.
At the project level, the default settings to the prior probabilities may be specified by selecting the Options > Pro6ect > Data Profiles main menu options, then selecting the Target Profile tab and the Class subtab. The baseline probability from the lift charts or performance charts is defined by these same prior probabilities. The purpose of the lift charts is to evaluate the performance of the classification model for binarh-balued targets. If there are no prior probabilities specified from the Prior tab, then the response rate o f t h e target event from the \,alidation data set is used as the baseline probability of the performance charts called lift charts.
Data Mining Using SAS Enterprise Miner by Randall Matignon