Prediction Probability in SQL Server Analysis Services

This document provides a comprehensive guide to understanding and utilizing prediction probability calculations within SQL Server Analysis Services (SSAS). Prediction probability allows you to assess the likelihood of a particular outcome occurring, based on historical data and predictive models.

Prediction probability is a core concept in data mining and predictive analytics. In SSAS, it's often associated with classification models, where you're predicting whether an instance belongs to a specific class or category. The probability represents the confidence level of the model's prediction.

Key Concepts:

• Class: The target outcome you are trying to predict (e.g., "Will Purchase," "Churn," "Fraud").
• Probability Score: A value between 0 and 1 (or 0% and 100%) indicating the likelihood of an instance belonging to a specific class. A score closer to 1 indicates a higher probability.
• Threshold: A pre-defined probability value used to make a final decision. If the probability score exceeds the threshold, the instance is assigned to the positive class; otherwise, it's assigned to the negative class.

SSAS provides several ways to retrieve and work with prediction probabilities, depending on the mining model type and the client application.

Using DMX (Data Mining Extensions)

When querying classification models (like Decision Trees, Naive Bayes, or Logistic Regression), you can use the PREDICTION_PROBABILITY function in DMX.

SELECT
    [TargetColumn],
    PREDICTION_PROBABILITY([TargetColumn], 'PositiveClassValue') AS ProbabilityOfPositive
FROM
    [YourMiningModel]
PREDICTION JOIN
    [YourDataSourceView] ON [YourMiningModel].[ForeignKey] = [YourDataSourceView].[ForeignKey]
WHERE
    [SomeCondition]
            

In this DMX query:

• [TargetColumn] is the column representing the predicted outcome.
• 'PositiveClassValue' is the specific value of the target column for which you want to calculate the probability (e.g., 'TRUE', '1', 'Yes').
• [YourMiningModel] is the name of your deployed mining model.
• [YourDataSourceView] and the PREDICTION JOIN are used to provide new data for prediction.

Using AMO (Analysis Management Objects)

Programmatically, you can use AMO to execute DMX queries and retrieve prediction probabilities.

Using MDX (Multidimensional Expressions) with OLAP Cubes

While primarily used for OLAP queries, MDX can interact with mining models deployed as part of a cube using the PredictProbability() function. This is less common for direct prediction probability retrieval compared to DMX but can be useful for scenario analysis within a cube context.

SELECT
    [SomeDimension].[Level].Members ON COLUMNS,
    -- Example of calling predict probability for a specific value
    -- Actual syntax might require more context/setup if model is not directly embedded
    -- More commonly, prediction results are consumed via DMX.
    [Measures].[YourPredictionMeasure] * Measures.Value AS [Probability Example]
FROM
    [YourCube]
WHERE
    [YourTimeDimension].[Year].&[2023]
            

The interpretation of probability scores is crucial for making informed decisions.

Common Scenarios:

• High Probability: Indicates a strong likelihood of the predicted outcome. For example, a 95% probability of churn suggests the customer is highly likely to leave.
• Low Probability: Indicates a low likelihood of the predicted outcome. A 5% probability of fraud suggests the transaction is unlikely to be fraudulent.
• Moderate Probability: May require further investigation or a business rule to determine the next steps.

Using Thresholds for Decision Making:

Setting an appropriate threshold is a business decision that balances the costs of false positives and false negatives.

You can implement threshold logic in your client application or by further processing the prediction results from SSAS.

• Model Accuracy: The reliability of prediction probabilities is directly tied to the accuracy and performance of the underlying mining model. Ensure your models are well-trained and validated.
• Data Quality: The quality of the data used to train and score the model significantly impacts probability calculations.
• Context is Key: Always consider the business context when interpreting probabilities. A "high" probability in one scenario might be considered "low" in another.
• Model Refresh: Regularly retrain and refresh your models to account for changing data patterns and maintain prediction accuracy.
• Tooling: Utilize SSAS tools (like SQL Server Data Tools or Visual Studio) for model development, deployment, and management.

• Predicting with Prediction Functions (SSAS Data Mining)
• DMX Statement Syntax
• Analysis Management Objects (AMO) Overview