[A Visual Introduction to Machine Learning] provides a simple, visual explanation of using decision trees:

> Recap:
>
> 1. Machine learning identifies patterns using statistical learning and computers by unearthing boundaries in data sets. You can use it to make predictions.
>
> 2. One method for making predictions is called a decision trees, which uses a series of if-then statements to identify boundaries and define patterns in the data
>
> 3. Overfitting happens when some boundaries are based on on distinctions that don't make a difference. You can see if a model overfits by having test data flow through the model.

The post, which is already ten months old, promises a second in the series on overfitting, but this short introduction is a fine standalone.

[A Visual Introduction to Machine Learning]: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ "A Visual Introduction to Machine Learning - Part 1"