Data Mining Extensions - DMX - Reference

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parameter list Optional. A comma-separated list of provider-defined parameters for the algorithm. filter criteria A filter expression that is applied to the columns in the case table. Remarks If the mining structure contains composite keys, the mining model must include all the key columns that are defined in the structure. If the model does not require a predictable column, for example, models that are built by using the Microsoft Clustering and Microsoft Sequence Clustering algorithms, you do not have to include a column definition in the statement. All the attributes in the resulting model will be treated as inputs. In the WITH clause that applies to the case table, you can specify options for both filtering and drillthrough: • Add the FILTER keyword and a filter condition. The filter applies to the cases in the mining model. • Add the DRILLTHROUGH keyword to enable users of the mining model to drill down from model results to the case data. In Data Mining Extensions (DMX), drillthrough can be enabled only when you create the model. To use both case filtering and drillthrough, you combine the keywords in a single WITH clause by using the syntax shown in the following example: WITH DRILLTHROUGH, FILTER(Gender = 'Male') Column Definition List You define the structure of a model by specifying a column definition list that includes the following information for each column: • Name (mandatory) • Alias (optional) • Modeling flags • Prediction request, which indicates to the algorithm whether the column contains a predictable value, indicated by the PREDICT or PREDICT_ONLY clause Use the following syntax for the column definition list to define a single column: [AS ] [] [] Column Name and Alias The column name that you use in the column definition list must be the name of the column as it is used in the mining structure. However, you can optionally define an alias to represent the structure column in the mining model. You can also create multiple column definitions for the same structure column, and assign a different alias and prediction usage to each copy of the column. By default, the structure column name is 43
used if you do not define an alias. For more information, see How to: Create an Alias for a Model Column. For nested table columns, you specify the name of the nested table, specify the data type as TABLE, and then provide the list of nested columns to include in the model, enclosed in parentheses. You can define a filter expression that is applied to the nested table by affixing a filter criteria expression after the nested table column definition. Modeling Flags Analysis Services supports the following modeling flags for use in mining model columns: Note The NOT_NULL modeling flag applies to the mining structure column. For more information, see CREATE MINING STRUCTURE (DMX). Term REGRESSOR MODEL_EXISTENCE_ONLY Definition Indicates that the algorithm can use the specified column in the regression formula of regression algorithms. Indicates that the values for the attribute column are less important than the presence of the attribute. You can define multiple modeling flags for a column. For more information about how to use modeling flags, see Modeling Flags (DMX). Prediction Clause The prediction clause describes how the prediction column is used. The following table lists the possible clauses. PREDICT PREDICT_ONLY This column can be predicted by the model, and its values can be used as input to predict the value of other predictable columns. This column can be predicted by the model, but its values cannot be used in input cases to predict the value of other predictable columns. 44
Page 2 and 3: Data Mining Extensions (DMX) Refere
Page 4 and 5: Contents Data Mining Extensions (DM
Page 6 and 7: * (Multiply) ......................
Page 8 and 9: • Create a mining structure by us
Page 10 and 11: The type of query you use depends o
Page 12 and 13: Each of these tasks uses a differen
Page 14 and 15: Back to Select Types Drillthrough Y
Page 16 and 17: Query type Supported functions Rema
Page 18 and 19: Query type Supported functions Rema
Page 20 and 21: An object identifier is created whe
Page 22 and 23: Data types depend on the algorithm.
Page 24 and 25: You can use operators to combine sm
Page 26 and 27: Operator Description argument on th
Page 28 and 29: Understanding the Select Statement
Page 30 and 31: DMX Syntax Conventions DMX Syntax E
Page 32 and 33: DMX Syntax Elements Understanding t
Page 34 and 35: Columns that are left unspecified i
Page 36 and 37: Data Mining Extensions (DMX) Statem
Page 38 and 39: Statement DROP MINING MODEL (DMX) D
Page 40 and 41: • Relationship to an attribute co
Page 42 and 43: ) Example 2: Specifying Holdout Per
Page 46 and 47: Filter Criteria Expressions You can
Page 48 and 49: [Model] = 'ML Road Tire' ) ) ) WITH
Page 50 and 51: For a list of the data types, conte
Page 52 and 53: [Model] TEXT DISCRETE PREDICT ) ) U
Page 54 and 55: object name Optional. The name of t
Page 56 and 57: parameter list Optional. A comma-se
Page 58 and 59: Arguments model A model identifier.
Page 60 and 61: If you use INSERT INTO with a minin
Page 62 and 63: ) SHAPE { OPENQUERY([AdventureWorks
Page 64 and 65: • SELECT FROM PREDICTION JOIN •
Page 66 and 67: Example results: Gender F M For col
Page 68 and 69: SELECT FROM .CONTENT Returns the mi
Page 70 and 71: Examples The following code returns
Page 72 and 73: DMX Data Manipulation Statements DM
Page 74 and 75: Not all mining model types support
Page 76 and 77: Remarks Algorithm providers define
Page 78 and 79: query to columns in the mining mode
Page 80 and 81: [HouseOwnerFlag], [NumberCarsOwned]
Page 82 and 83: expression list n A comma-separated
Page 84 and 85: Example 1: Drill through to Structu
Page 86 and 87: query syntax A query syntax that re
Page 88 and 89: ... RELATE TO ) AS < column table
Page 90 and 91: esults of the prediction are return
Page 92 and 93: Function RangeMax RangeMid RangeMin
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Model $SUPPORT $PROBABILITY $ADJUST
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A column in the nested table, or ex
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example uses $SUPPORT because suppo
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Examples The following example uses
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'Graduate Degree' AS [Education], 0
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For more information about using th
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A Boolean type. Remarks IsDescendan
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Functions (DMX) Querying Data Minin
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Predict(, [option1], [option2], [op
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Syntax PredictAdjustedProbability(,
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See Also Mapping Functions to Query
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PredictHistogram( | ) Applies To A
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Return Type Examples The following
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Bike Buyer Bike Buyer = Yes Bike Bu
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PredictSupport Returns the support
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n-start, n-end Specifies a range of
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this query returns two columns. The
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WHERE ([Model Region] = 'M200 Europ
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Model Region Europe M200 Europe M20
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Scalar columns. Return Type A scala
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Scalar columns. Return Type A scala
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We will use the following mining st
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product name, but also returns the
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Applies To An expression that retur
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The third argument to the TopPercen
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Operator Description argument or bo
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+ (Add) Performs an arithmetic oper
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Operators (DMX) Data Mining Extensi
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Return Value A Boolean value that c
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Return Value A Boolean value that c
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NOT A logical operator that perform
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Syntax + Numeric_Expression Paramet
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-- Comment_Text Parameters Paramete
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Convention Usage syntax that can be
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Data Mining Extensions - DMX - Reference

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