Explore my implementation of a Decision Tree from scratch, available on my Github repository: This repository was initially created as a part of a course project: Find more details here

Introduction

Decision trees are widely used in machine learning for classification and prediction tasks. They offer an interpretable and transparent model that clearly explains the reasoning behind each prediction. This makes decision trees appealing to practitioners who need to understand and justify the model’s outputs.

In this post, we will walk through the process of building a decision tree from scratch. We’ll cover the key components and algorithms involved. By the end, you will have a solid grasp of how decision trees work under the hood.

What are Decision Trees?

A decision tree is a flowchart-like structure where each internal node represents a feature, each branch is a decision rule, and each leaf node contains the final prediction or class label. The tree is constructed by recursively splitting the data based on feature values to maximize information gain at each node.

Key Advantages of Decision Trees

1. Easy to understand and interpret
2. Requires little data preparation
3. Can handle both numerical and categorical data
4. Performs well even with large datasets
5. Forms the basis for ensemble methods like Random Forests

Building a Decision Tree Step-by-Step

Now let’s dive into the process of constructing a decision tree from scratch. We’ll use a simple dataset for illustration.

Step 1: Preparing the Data

• Load the dataset into a pandas DataFrame
• Handle missing values and outliers
• Encode categorical variables as numbers

Step 2: Splitting the Data

• Divide the dataset into training and testing sets
• Use stratified sampling to ensure class balance

Step 3: Choosing the Best Split

• For each feature, calculate information gain if split on that feature
• Information gain measures the reduction in entropy or impurity
• Select the feature with maximum information gain to split the node

Step 4: Recursively Building the Tree

• Repeat steps 3 at each child node until a stopping criteria is met:
  • All samples in a node have the same class
  • No remaining features to split on
  • Reached max depth of tree
• Assign majority class of samples in leaf node as its label

Step 5: Making Predictions

• To classify a new sample, traverse the tree based on its feature values
• The leaf node reached contains the predicted class label

Conclusion

In this post, we covered the basics of building a decision tree classifier from scratch. We walked through the key steps of data preparation, recursive tree building using information gain, and making predictions on new data.

Decision trees offer a simple yet effective approach to machine learning. Their interpretability makes them useful across many domains. I encourage you to explore the linked code repository to see the complete implementation.

In the next post, we’ll provide a script to generate custom datasets for experimentation. Stay tuned! As always, feel free to reach out with any questions.