Decision trees are the most intuitive among machine learning algorithms. We use decision trees in daily life all the time.
Decision tree algorithms have a lot of useful features:
Easy to understand and interpret
Work with both categorical and continuous features
Work with missing features
Do not require feature scaling
Decision tree algorithms work in an upside-down order in which an expression containing a feature is evaluated at every level and that splits the dataset into two categories. We'll help you understand this with the simple example of a dumb charade, which most of us played in college. I guessed an animal and asked my coworker ask me questions to work out my choice. Here's how her questioning went:
Q1: Is it a big animal?
A: Yes
Q2: Does this animal live more than 40 years?
A: Yes
Q3: Is this animal an elephant?
A: Yes
This is an obviously oversimplified case in which she knew I had postulated an elephant (what else would you guess in a Big Data...