The decision Tree algorithm is a supervised **Machine Learning** algorithm used for both classification and regression problems. It is based on a sequential decision process. If you want to know everything about the Decision Tree algorithm, this article is for you. This article will introduce the Decision Tree algorithm, how it works, and everything you should know about it.

## Introduction to Decision Tree Algorithm

The Decision Tree algorithm is based on a sequential decision process that works like a flowchart-like tree structure. It works by recursively partitioning the data into subsets based on the values of the input features. Then a decision rule is applied to assign a label to the data at each step. The final result is a tree model that makes predictions on new data.

The highest node in a decision tree is called the root node. That is where the process of this algorithm begins. The process starts at the root node, and then one of the two branches is selected by evaluating the feature. The process repeats until the last leaf is reached. This point describes the final output called the target variable or label.

## Here’s How Decision Tree Algorithm Works

Here’s how the decision trees work:

- Input Feature Selection: The algorithm starts at the tree’s root node and selects the feature that best separates the data into subsets based on the target variable.
- Decision rule creation: The algorithm creates a decision rule based on the selected feature (used to divide the data into subsets). This rule can be a simple comparison, such as “x < 10”, or a more complex comparison, such as “x in [1, 2, 3]”.
- Creation of child nodes: For each subset created by the decision rule, a child node is created, and then the process is repeated for each child node. This process is repeated until the stopping criterion is satisfied.
- Label assignment: Once the tree is fully grown, each leaf node is assigned a label based on the majority class of data points that reaches that node.
- Make predictions: The algorithm starts at the root node and applies the decision rule to each node going down the tree until it reaches a leaf node. The label assigned to this leaf node is nothing but the final prediction.

## Advantages and Disadvantages of Decision Trees

Below are the advantages and the disadvantages of the Decision Trees you should know:

**Advantages:**

- The tree-like structure of Decision Trees is easy to understand and can be visualized to see how the decisions are being made.
- Decision Trees are comparatively fast to train and make predictions than other algorithms.

**Disadvantages:**

- Sometimes decision trees easily overfit the training data making the model unable to generalize well on unseen data.
- Decision trees are sensitive to unbalanced datasets resulting in a biased model towards the dominant class.

You can learn the implementation of the Decision Trees using Python **here**.

### Summary

So the Decision Tree algorithm is a supervised Machine Learning algorithm used for both classification and regression problems. It is based on a sequential decision process. I hope you liked this article on the Decision Tree algorithm. You can learn about the implementation of the Decision Trees using Python **here**. Feel free to ask valuable questions in the comments section below.