Model Evaluation (Analysis Services Data Mining)

Introduction to Model Evaluation

Evaluating the performance of your data mining models is a critical step in the data mining lifecycle. It allows you to understand how well your model predicts outcomes, identify its strengths and weaknesses, and compare it against alternative models or baseline scenarios. Analysis Services provides a rich set of tools and metrics for model evaluation.

Why Evaluate Models?

Assess Predictive Accuracy: Determine how closely the model's predictions match actual outcomes.
Compare Models: Objectively compare different algorithms or different configurations of the same algorithm.
Identify Overfitting/Underfitting: Detect if the model is too complex (overfitting) or too simple (underfitting) for the data.
Business Understanding: Translate technical metrics into business insights to inform decision-making.
Model Selection: Choose the best-performing model for deployment.

Key Evaluation Metrics

Classification Models

For classification models, common evaluation metrics include:

Accuracy: The proportion of correct predictions out of all predictions.
Accuracy = (True Positives + True Negatives) / Total Instances
Precision: The proportion of true positives out of all instances predicted as positive.
Precision = True Positives / (True Positives + False Positives)
Recall (Sensitivity): The proportion of true positives out of all actual positive instances.
Recall = True Positives / (True Positives + False Negatives)
F1 Score: The harmonic mean of precision and recall, providing a single measure.
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
Confusion Matrix: A table that summarizes the prediction results of a classification model. It shows the counts of true positives, true negatives, false positives, and false negatives.
ROC Curve and AUC: The Receiver Operating Characteristic (ROC) curve plots the true positive rate against the false positive rate. The Area Under the Curve (AUC) is a common measure of the classifier's performance. A higher AUC indicates a better model.

Clustering Models

For clustering models, evaluation often focuses on:

Cluster Quality Metrics: Such as silhouette scores or Davies-Bouldin index, which measure how well-separated and compact the clusters are.
Cluster Profiling: Analyzing the characteristics of members within each cluster to understand their distinct properties.
Drillthrough Capabilities: Examining individual data points within clusters to validate their assignment.

Regression Models

For regression models, key metrics include:

Mean Absolute Error (MAE): The average of the absolute differences between predicted and actual values.
MAE = Sum(|Actual - Predicted|) / Number of Instances
Mean Squared Error (MSE): The average of the squared differences between predicted and actual values. Penalizes larger errors more heavily.
MSE = Sum((Actual - Predicted)^2) / Number of Instances
Root Mean Squared Error (RMSE): The square root of MSE, bringing the error metric back to the original units of the target variable.
RMSE = sqrt(MSE)
R-squared: The coefficient of determination, representing the proportion of the variance in the dependent variable that is predictable from the independent variables.

Using Analysis Services for Evaluation

DMX Queries

You can use Data Mining Extensions (DMX) queries to retrieve prediction results and calculate custom metrics.

SELECT
    [Customer],
    [Predicted <Customer> Buy],
    [CAST([Customer] AS FLOAT) = [Predicted <Customer> Buy]] AS CorrectPrediction
FROM
    [MyPredictionQuery]
PREDICTION JOIN
    [MyCustomerTable] ON [MyCustomerTable].[Customer] = [MyPredictionQuery].[Customer]
WHERE
    [MyCustomerTable].[CustomerID] = 12345;

SQL Server Management Studio (SSMS) Tools

SSMS provides graphical tools for model evaluation:

Mining Model Viewer: Different tabs are available for each model type (Decision Tree Viewer, Cluster Viewer, Regression Viewer, etc.) that display model structure and performance characteristics.
Mining Accuracy Chart: Visualizes the performance of classification models by plotting cumulative gains or lift charts.
Confusion Matrix: Directly viewable in SSMS for classification models.

Tip: Always evaluate your model on a separate test dataset that was not used during training to get an unbiased estimate of its performance.

Cross-Validation

Cross-validation is a powerful technique to assess how a model generalizes to an independent dataset. Analysis Services supports cross-validation, allowing you to divide your data into multiple folds, train the model on a subset of folds, and test it on the remaining fold. This process is repeated multiple times, and the results are averaged to provide a more robust evaluation.

Best Practice: When evaluating models, consider both statistical metrics and their business implications. A model with slightly lower statistical accuracy might be preferred if it provides more actionable insights or is more efficient to implement.