Doing classification using decision trees

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...