Decision trees are the most intuitive among machine-learning algorithms. We use decision trees in our daily lives 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 this splits the dataset into two categories. We will help you understand this with a simple dumb charades example, which most of us may have played in college. I guessed an animal and asked my coworker to ask me questions to work out my choice. Here's how her questioning went:
- Q1: Is it a big animal?
Answer: Yes.
- Q2: Does this animal live for more than 40 years?
Answer: Yes.
- Q3: Is this animal an elephant?
Answer: Yes.
This is obviously an oversimplified case in which she knew I had postulated an elephant (what...