The if a: else b statement is one of the most common statements in Python programming. By nesting and combining such statements, we can build a so-called decision tree. This is similar to an old fashioned flowchart, although flowcharts also allow loops. The application of decision trees in machine learning is called decision tree learning. The end nodes of the trees in decision tree learning, also known as leaves, contain the class labels of a classification problem. Each non-leaf node is associated with a Boolean condition involving feature values.
Decision trees can be used to deduce relatively simple rules. Being able to produce such results is, of course, a huge advantage. However, you have to wonder how good these rules are. If we add new data, would we get the same rules?
If one decision tree is good, a whole forest should be even better. Multiple trees should reduce the chance of overfitting. However, as in a real forest, we don't want only one type of tree...