SQL Server Analysis Services

Scoring in Analysis Services

This section provides an in-depth guide to scoring in SQL Server Analysis Services (SSAS). Scoring refers to the process of applying a trained data mining model to new data to generate predictions or insights.

Understanding Scoring

When you build a data mining model in SSAS, the ultimate goal is often to use that model to make predictions or classify new instances. The process of using a trained model on new data is called scoring.

Key Concepts

• Prediction Query: A query that uses a mining model to generate predictions.
• Input Data: The new data that you want to score. This data should have the same structure and relevant columns as the data used to train the model.
• Output: The results of the scoring process, which can include predicted values, probabilities, cluster assignments, or associations.

Types of Scoring

Analysis Services supports various scoring scenarios:

1. DMX (Data Mining Extensions) Scoring

DMX is a query language specifically designed for data mining operations in Analysis Services. You can use DMX to create prediction queries.

Example DMX Prediction Query (for a classification model):

SELECT
    [Sample-Sport].[Sport] AS PredictedSport,
    PredictProbability([Sample-Sport].[Sport]) AS Probability
FROM
    [Sample-Sport]
PREDICTION JOIN
    [dbo].[NewCustomerData] AS t ON [Sample-Sport].[Gender] = t.[Gender]
WHERE
    t.[CustomerID] = 'CUST12345'

2. Client Application Integration

You can integrate SSAS scoring directly into your client applications (e.g., .NET, Python, R) using the Analysis Services client libraries. This allows for real-time scoring.

3. Stored Procedures and Scripting

SSAS also allows you to embed scoring logic within stored procedures or execute scoring operations via scripts, providing automation and batch processing capabilities.

Scoring Scenarios

• Customer Churn Prediction: Predict which customers are likely to stop using a service.
• Product Recommendation: Recommend products to customers based on their purchase history.
• Fraud Detection: Identify potentially fraudulent transactions.
• Lead Scoring: Prioritize sales leads based on their likelihood to convert.

Best Practices for Scoring

• Ensure input data quality and consistency.
• Understand the confidence and probability of predictions.
• Regularly retrain models with new data to maintain accuracy.
• Monitor the performance of scored predictions over time.