Decision Trees — An Intuitive Introduction

What is Decision Tree?

Decision Trees are versatile machine learning algorithms that can perform classification and regression problems. They are powerful algorithms, capable of fitting complex datasets.

Decision trees are also the fundamental components of random forests, which are among the most powerful machine learning algorithms available today.

Advantages of Decision Tree

1. Intuitive and easy to understand.
2. Minimum data preparation is required.
3. The cost of using the tree is logarithmic in the number of data points used to train the tree.

Disadvantages of Decision Tree

1. Overfitting
2. Prone to errors for in-balance datasets.

Theoretical Institution of decision tree

By seeing the above two examples we can conclude that Decision trees are nothing but nested if-else statements.

Geometric Intuition Of Decision Tree

Mathematically speaking, Decision tree use hyperplanes that run parallel to any one of the axes to cut your coordinate system into hyper cuboids.

Terminology For Decision Tree

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