A decision tree is a simple, highly visual, and a very powerful analytical tool that analyses your data and builds for you a tree of nodes. Each node represents a logical decision, such as a choice of a value of one of your inputs (such as age, or income) that makes the most profound difference to the output that you wish to study (such as customer profitability). This short, free, 10-minute video by Rafal introduces this useful tool in an easy way, by explaining the concepts while analysing a simple set of retail data in the demo.

Microsoft Decision Trees can be used for three different purposes, without having to modify your data much! You can use them for: classification, regression, and even for associative analysis, which is similar to Association Rules technique, typically used for Market Basket Analysis. Without doubt, however, the simplest, flattened-data (case-level) decision trees are one of the best ways to start analysing any data, before proceeding to the use of other techniques, with the possible exception of the Naive Bayes and Clustering techniques, which can be useful especially when you suspect that the data set is odd, or perhaps it is one you do not understand well.

You will see how to create a simple decision tree using Excel and the free Data Mining Add-ins for Office (1:56), which connect Excel to a running instance of SQL Server Analysis Services 2012, 2008 R2, or 2008—if you use 2012, you need to be running a multidimensional instance for data mining to work. After explaining what the results mean, you will briefly see two more complex decision trees, including a regressive and an associative (nested) decision tree in the recently introduced SQL Server Data Tools 2012 (8:33).

If you are interested in learning about data mining, make sure to follow our entire tutorial, which includes a comprehensive, 2-hour module Decision Trees In Depth, available to our Full Access Members, who can also download our training data set, Happy Cars, which makes it possible for you to follow the demos.