In This Article

This document provides a comprehensive overview of classification algorithms available in SQL Server Analysis Services (SSAS) and how to use them for data mining tasks.

Introduction to Classification

Classification is a supervised data mining technique used to predict a categorical or discrete target variable based on one or more predictor variables. In SSAS, classification algorithms are used to build predictive models that can assign new data instances to predefined categories or classes.

Key applications of classification include:

  • Customer churn prediction (e.g., will a customer leave or stay?)
  • Fraud detection (e.g., is a transaction fraudulent or legitimate?)
  • Spam filtering (e.g., is an email spam or not spam?)
  • Medical diagnosis (e.g., does a patient have a particular disease?)

Types of Classification Algorithms

SQL Server Analysis Services offers several built-in classification algorithms, each with its own strengths and weaknesses. The choice of algorithm often depends on the characteristics of your data and the desired model complexity.

Decision Trees

Decision trees are a popular and intuitive classification algorithm. They work by recursively partitioning the data into subsets based on the values of predictor variables. The resulting model is a tree-like structure where each internal node represents a test on an attribute, each branch represents an outcome of the test, and each leaf node represents a class label.

  • Pros: Easy to understand and interpret, can handle both numerical and categorical data, provides clear rules.
  • Cons: Can be prone to overfitting, may not perform well with complex relationships.
Tip: Decision trees are often a good starting point due to their interpretability.

Logistic Regression

Logistic regression is a statistical method used for binary classification problems (predicting one of two outcomes). It models the probability of a particular outcome occurring as a function of the predictor variables using a logistic function.

  • Pros: Provides probability estimates, efficient for binary classification, relatively easy to implement.
  • Cons: Assumes a linear relationship between predictors and the log-odds of the outcome, can be sensitive to outliers.

Neural Networks

Neural networks, specifically Multilayer Perceptrons (MLPs) in SSAS, are powerful algorithms inspired by the structure of the human brain. They consist of interconnected layers of nodes (neurons) that learn complex patterns in data. They are highly effective for non-linear relationships.

  • Pros: Can model very complex relationships, robust to noisy data, can handle large datasets.
  • Cons: Often considered a "black box" due to lack of interpretability, requires significant data and computational resources, sensitive to hyperparameter tuning.
Note: For multi-class classification, neural networks in SSAS can be configured appropriately.

Support Vector Machines (SVM)

Support Vector Machines are a powerful algorithm that finds an optimal hyperplane to separate data points belonging to different classes. SVMs can handle both linear and non-linear classification tasks using kernel functions.

  • Pros: Effective in high-dimensional spaces, memory efficient, versatile due to different kernel functions.
  • Cons: Can be computationally intensive, selecting the right kernel and parameters can be challenging, less interpretable than decision trees.

Using Classification Algorithms in SSAS

To use classification algorithms in SSAS, you typically follow these steps:

  1. Create a Data Mining Project: In SQL Server Data Tools (SSDT) or Visual Studio, create a new Analysis Services project.
  2. Create a Data Source and Data Source View: Connect to your data source and define a view of the relevant tables and columns.
  3. Create a Mining Structure: Define the structure of your mining model, specifying the input columns (predictors) and the predictable column (target).
  4. Choose a Mining Algorithm: Select the desired classification algorithm (e.g., Decision Trees, Neural Network) for your mining structure.
  5. Train the Model: Process the mining structure to train the model on your data. SSAS will generate the model based on the selected algorithm and data.
  6. Explore and Predict: Use the mining viewer to explore the trained model's insights and use the mining model to make predictions on new data.
Important: Ensure your target column is a discrete or categorical data type for classification tasks.

Algorithm Parameters

Each classification algorithm in SSAS has specific parameters that can be tuned to influence the model's performance. For example:

  • Decision Trees:
    • MAX_DEPTH: Maximum depth of the decision tree.
    • MINIMUM_SUPPORT: Minimum number of training cases required to create a branch.
    • SPLIT_METHOD: Method used for splitting nodes (e.g., AUTO, Best_Cut, Gini).
  • Neural Networks:
    • HIDDEN_LAYERS: Number and configuration of hidden layers.
    • MAX_ITERATIONS: Maximum number of training iterations.
    • LEARNING_RATE: Controls the step size during weight updates.

Refer to the MSDN documentation for specific algorithm parameters for detailed information.