Understanding decision trees

I chose to start this book with decision trees because I've noticed that the majority of new machine learning practitioners have previous experience in one of two fields—software development, or statistics and mathematics. Decision trees can conceptually resemble some of the concepts software developers are used to, such as nested if-else conditions and binary search trees. As for the statisticians, bear with me—soon, you will feel at home when we reach the chapter about linear models.

What are decision trees?

I think the best way to explain what decision trees are is by showing the rules they generate after they are trained. Luckily, we can access those rules and print them. Here is an example of how decision tree rules look:

Shall I take an umbrella with me?
|--- Chance of Rainy <= 0.6
|    |--- UV Index <= 7.0
|    |    |--- class: False
|    |--- UV Index >  7.0
|    |    |--- class: True
|--- Chance...