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【正文】 it Risk: 45% Good 55% Bad Debt=High Debt ? Employ ment Type? ET = Self Employed Education= College Customers having high debt and college education: Filter([Individual Customers].Members, (“Debt”) = “High” And (“Education”) = “College”) Customers having low debt and are self employed: Filter([Individual Customers].Members, (“Debt”) = Low And (“Employment Type”) = “Self Employed”) …Equivalent DM Dimension Customers with high debt and college education All Customers Customers with high debt Customers with high debt and high school education Customers with low debt and self employed Customers with low debt Customers with low debt and salaried Custom Rollup Credit Risk Good = 65%, Bad = 35% Aggregate(Filter(… Good = 89%, Bad = 11% Aggregate(Filter(… Good = 79%, Bad = 21% Aggregate(Filter(… Good = 94%, Bad = 6% Aggregate(Filter(… Good = 45%, Bad = 55% Aggregate(Filter(… Good = 70%, Bad = 30% Aggregate(Filter(… Good = 31%, Bad = 69% Tree = Dimension ? Every node on the tree is a dimension member ? The node statistics are the member properties ? All members are calculated ? Formula aggregates the case dimension members that apply to this node ? The MDX is generated by the DM algorithm ? Analysis Service will automatically generate the calculated dimension based on the DM content and also a virtual cube ? Applies to ? Classification (decision trees) ? Segmentation (clusters) Browsing the Virtual Cube ? Pivot the DM dimension: WA OR CA All Customers 3200 2500 8000 Customers with low debt 2320 1503 4300 Customers with high debt 880 997 4700 Customers … college 320 450 2310 Customers … high school 560 547 2390 Credit Risk: 70% Good, 30% Bad Predictions ? You might want to view predictions for each case ? For example: ? What is the expected profitability of a product? ? What is the credit risk of a specific customer? ? What are the products this customer is likely to buy? ? All of those predictions are available through MDX calculated members ? Singleton query is created automatically Prediction Calculated Member Measures.[Probability of High Credit Risk]: PREDICT(, “Credit Risk Model”, “PredictionProbability( PredictionHistogram(“Credit Risk”), ?High?)“ ) Predictions Example Probability of High Credit Risk Probability of Low Credit Risk Joe Smith 73% 27% John Dow 68% 32% William Clington 45% 55% Robert Maxwell 98% 2% Denis Rodman 81% 19% Questions ? EMail:
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