Why Decision Trees?
Explore the fundamentals of decision trees, including their intuitive structure, ease of use with various data types, and versatility in handling classification and regression tasks. Understand why decision trees form the foundation for advanced algorithms like random forest and XGBoost, making them an ideal starting point for mastering machine learning.
A fundamental algorithm
Decision trees are a fundamental machine learning algorithm taught in many (if not all) university courses on machine learning, data science, and data mining. Decision trees are considered a fundamental algorithm for three reasons:
They’re simple to learn.
They’re easy to use.
They’re the basis for the most commonly used state-of-the-art machine learning algorithms.
For the reasons listed above, decision trees are the best place for aspiring data scientists to get started with machine learning.
Simple to learn
Two aspects of decision trees make them simple to learn—they’re intuitive and the mathematics of decision trees is straightforward.
Decision trees are intuitive
Decision trees are relatively simple to learn compared to many machine learning algorithms. First, almost everyone is familiar with decision trees, especially when visually represented:
Decision trees are traversed from top to bottom, where the path is split into left and right branches based on a series of questions (or decisions / rules). The intuitive nature of trees makes them a perfect first machine learning algorithm.
Notice how extensive domain knowledge is not needed to ...