How to explain decision tree algortihm in layman's terms?
I learn well by examples, so perhaps this will be helpful:

Lets say you are a resort considering your next advertizing campaign. You will be sending out 1,000 vouchers that give two free days at the resort, with the hope that the customer will decide to extend it to a full week (who want's a two day vacation?!). 

You have a rather extensive database of customer data, including where they are from, income range, duration of stay(s) and money spent at each stay, number of children, marital status, and employment status (working, unemployed, retired). You would like to create a decision tree to help select the customers to send the vouchers to (specifically, those who will spend at least $200 per day and stay at least one week.

To create the tree, a computer will perform the following steps:


From the above list of customer data, find the property that best separates the customers into two groups.
Repeat step one for each of the above groups using the remaining properties.
In the end, you will have a tree where at each point, you can make one of two decisions. Following a path leads to a decision. The split points will be chosen to maximize the probability of a correct classification.