Understanding Random Forests from Scratch

 In today’s world of data science, tools are needed to make highly accurate predictions and classifications from vast amounts of data.

Among these, “Random Forest” stands out as a simple yet powerful algorithm attracting attention in many fields. 

This article explains the basic concepts of Random Forest, specific use cases, and the benefits of learning this technology.

1. What is a Random Forest?

A Random Forest is an ensemble learning method that generates multiple decision trees and integrates their prediction results to achieve high accuracy.

As the name suggests, a Random Forest constructs a “forest” of many decision trees. Each decision tree is created from a random sample of the original data (bootstrap sample), and the features used at each branching point are also randomly selected. This reduces the variance and risk of overfitting of individual trees, resulting in a very robust model overall.

In classification problems, a majority vote (the most frequent class output by the decision trees) is used, and in regression problems, the average of the predicted values of each tree is taken, leading to more stable results.

This ensemble strategy makes Random Forests more resistant to data noise and complex patterns compared to a single decision tree.

2. What Fields is it Used In?

Random Forests, due to their versatility and high accuracy, have delivered concrete results in various fields. Here are some examples

- Healthcare and Bioinformatics

It’s used to analyze high-dimensional data such as patient data and genetic information, aiding in disease prediction and diagnosis.  In the medical field, where different cases and factors are intertwined, the strengths of Random Forest are greatly beneficial.

- Financial Industry

It's employed in analyzing complex patterns in financial data for credit scoring, fraud detection, and investment risk assessment.  In the financial sector, which involves numerous uncertainties, Random Forest is trusted as a robust prediction technology.

- Marketing and Customer Relations

It analyzes customer purchase history and web access data to reveal patterns in consumer behavior, helping to formulate targeted marketing strategies. The ability to understand customer needs from diverse variables is a significant asset for businesses.

- Image and Speech Recognition

Random Forests excel at feature selection, making them effectively used in the preprocessing stage of image and speech data, contributing to the detection and classification of complex patterns.

These achievements in various fields demonstrate the flexibility and high performance of Random Forests, and further applications are expected in the future.

3. What are the Benefits of Learning Random Forest?

Learning Random Forest offers many benefits for improving practical skills in data analysis and machine learning.

- High Prediction Accuracy and Stability

It reduces the risk of overfitting, which can occur with a single decision tree, allowing you to build robust prediction models even with real-world data.

- Interpretability and Feature Importance Analysis

Random Forests can evaluate the contribution of each feature. This allows you to visualize which factors have the biggest impact on prediction results, helping you gain important insights in business and research settings.

- Immediate Application to Practical Work

With a basic understanding, it’s possible to customize it for diverse datasets and problem settings. Because there are many practical examples of its application in different industries, such as finance, healthcare, and marketing, mastering it can help enhance your presence at work.

- Smooth Transition from Beginner to Advanced

Random Forest is a model based on decision trees, making it an ideal stepping stone to understanding other ensemble methods in machine learning (e.g., boosting and gradient boosting). This broadens your knowledge of machine learning as a whole, allowing you to smoothly transition to the next step.

Summary

Random Forest is a powerful ensemble learning method created to overcome the weaknesses of a single decision tree and capture patterns in complex data. It has been put into practical use in diverse fields such as healthcare, finance, and marketing, and its high accuracy and interpretability are major strengths in solving real-world problems.

By learning this technology, you can significantly improve your modern data analysis skills and acquire the ability to respond flexibly to any situation. Becoming proficient in Random Forest will also deepen your understanding of advanced machine learning technologies and become a knowledge base that directly leads to career advancement. As a next step, we recommend actually implementing Random Forest with programming. By coming into contact with new knowledge and technologies, your journey in data science will broaden and deepen.

If you want to learn Random Forest, we recommend this book (access here).