Querying a Naive Bayes Model (Analysis Services - Data Mining)

By querying the data mining schema rowset, you can find metadata for the model. This might include when the model was created, when the model was last processed, the name of the mining structure that the model is based on, and the name of the columns used as the predictable attribute. You can also return the parameters that were used when the model was created.

SELECT MODEL_CATALOG, MODEL_NAME, DATE_CREATED, LAST_PROCESSED,
SERVICE_NAME, PREDICTION_ENTITY, FILTER
FROM $system.DMSCHEMA_MINING_MODELS
WHERE MODEL_NAME = 'TM_NaiveBayes_Filtered'

Sample results:

The model used for this example is based on the Naive Bayes model you create in the Basic Data Mining Tutorial, but was modified by adding a second predictable attribute and applying a filter to the training data.

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In a Naive Bayes model, the marginal statistics node stores aggregated information about the distribution of values in the training data. This summary is convenient and saves you from having to create SQL queries against the training data to find the same information.

The following example uses a DMX content query to retrieve the data from the node (NODE_TYPE = 24). Because the statistics are stored in a nested table, the FLATTENED keyword is used to make the results easier to view.

SELECT FLATTENED MODEL_NAME,
(SELECT ATTRIBUTE_NAME, ATTRIBUTE_VALUE, [SUPPORT], [PROBABILITY], VALUETYPE FROM NODE_DISTRIBUTION) AS t
FROM TM_NaiveBayes.CONTENT
WHERE NODE_TYPE = 26

Partial results:

For example, these results tell you the number of training cases for each discrete value (VALUETYPE = 4), together with the computed probability, adjusted for missing values (VALUETYPE = 1).

For a definition of the values provided in the NODE_DISTRIBUTION table in a Naive Bayes model, see Mining Model Content for Naive Bayes Models (Analysis Services - Data Mining). For more information about how support and probability calculations are affected by missing values, see Missing Values (Analysis Services - Data Mining).

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Because a Naive Bayes model often contains complex information about the relationships among different attributes, the easiest way to view these relationships is to use the Microsoft Naive Bayes Viewer. However, you can create DMX queries to return the data.

The following example shows how to return information from the model about a particular attribute, Region.

SELECT NODE_TYPE, NODE_CAPTION, 
NODE_PROBABILITY, NODE_SUPPORT, MSOLAP_NODE_SCORE
FROM TM_NaiveBayes.CONTENT
WHERE ATTRIBUTE_NAME = 'Region'

This query returns two types of nodes: the node that represents the input attribute (NODE_TYPE = 10), and nodes for each value of the attribute (NODE_TYPE = 11). The node caption is used to identify the node, rather than the node name, because the caption shows both the attribute name and attribute value.

Some of the columns stored in the nodes are the same that you can get from the marginal statistics nodes, such as the node probability score and the node support values. However, the MSOLAP_NODE_SCORE is a special value provided only for the input attribute nodes, and indicates the relative importance of this attribute in the model. You can see much the same information in the Dependency Network pane of the viewer; however, the viewer does not provide scores.

The following query returns the importance scores of all attributes in the model:

SELECT NODE_CAPTION, MSOLAP_NODE_SCORE
FROM TM_NaiveBayes.CONTENT
WHERE NODE_TYPE = 10
ORDER BY MSOLAP_NODE_SCORE DESC

Sample results:

By browsing the model content in the Microsoft Generic Content Tree Viewer, you will get a better idea of what statistics might be interesting. Some simple examples were demonstrated here; more often you may need to execute multiple queries or store the results and process them on the client.

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In addition to writing your own content queries, you can use some Analysis Services system stored procedures to explore the results. To use a system stored procedure, prefix the stored procedure name with the CALL keyword:

CALL GetPredictableAttributes ('TM_NaiveBayes')

Partial results:

For more information about Analysis Services system stored procedures, see Data Mining Stored Procedures (Analysis Services - Data Mining).

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The following query uses a singleton query to provide a new value and predict, based on the model, whether a customer with these characteristics is likely to buy a bike. The easiest way to create a singleton query on a regression model is by using the Singleton Query Input dialog box. For example, you can build the following DMX query by selecting the TM_NaiveBayes model, choosing Singleton Query, and selecting values from the dropdown lists for [Commute Distance] and Gender.

SELECT
  Predict([TM_NaiveBayes].[Bike Buyer])
FROM
  [TM_NaiveBayes]
NATURAL PREDICTION JOIN
(SELECT '5-10 Miles' AS [Commute Distance],
  'F' AS [Gender]) AS t

Example results:

The prediction function returns the most likely value, in this case, 0, which means this type of customer is unlikely to purchase a bike.

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In addition to predicting an outcome, you often want to know how strong the prediction is. The following query uses the same singleton query as the previous example, but adds the prediction function, PredictHistogram (DMX), to return a nested table that contains statistics in support of the prediction.

SELECT
  Predict([TM_NaiveBayes].[Bike Buyer]),
  PredictHistogram([TM_NaiveBayes].[Bike Buyer])
FROM
  [TM_NaiveBayes]
NATURAL PREDICTION JOIN
(SELECT '5-10 Miles' AS [Commute Distance],
  'F' AS [Gender]) AS t

Example results:

The final row in the table shows the adjustments to support and probability for the missing value. Variance and standard deviation values are always 0, because Naive Bayes models cannot model continuous values.

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The Microsoft Naive Bayes algorithm can be used for association analysis, if the mining structure contains a nested table with the predictable attribute as the key. For example, you could build a Naive Bayes model by using the mining structure created in Lesson 3: Building a Market Basket Scenario (Intermediate Data Mining Tutorial) of the data mining tutorial. The model used in this example was modified to add information about income and customer region in the case table.

The following query example shows a singleton query that predicts products that are related to purchases of the product, 'Road Tire Tube'. You might use this information to recommend products to a specific type of customer.

SELECT   PredictAssociation([Association].[v Assoc Seq Line Items])
FROM [Association_NB]
NATURAL PREDICTION JOIN
(SELECT 'High' AS [Income Group],
  'Europe' AS [Region],
  (SELECT 'Road Tire Tube' AS [Model]) 
AS [v Assoc Seq Line Items]) 
AS t

Partial results:

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