Introduction to Random Forest and Support Vector Machine (SVM)

In the field of Machine Learning, two of the most widely used and powerful algorithms for classification and prediction are Random Forest and Support Vector Machine (SVM). Both play an essential role in data-driven decision-making and pattern recognition across industries such as healthcare, finance, environment, and technology.

🌳 Random Forest

Random Forest is an ensemble learning algorithm that combines multiple decision trees to produce more accurate and stable predictions. Instead of relying on a single decision tree, Random Forest builds a "forest" of trees and takes the majority vote (for classification) or average (for regression) as the final output.Key features:

• Reduces overfitting compared to a single decision tree
• Works well with both numerical and categorical data
• Provides good accuracy even with large datasets

⚙️ Support Vector Machine (SVM)

Support Vector Machine (SVM) is a supervised learning algorithm mainly used for classification tasks. It works by finding the best boundary (called a hyperplane) that separates data points of different classes in an N-dimensional space.Key features:

• Effective in high-dimensional spaces
• Works well for both linear and non-linear data (using kernel functions)
• Robust against overfitting, especially in smaller datasets

Together, Random Forest and SVM represent two strong yet complementary approaches in machine learning — one focusing on ensemble decision-making and the other on optimal separation of data.📘 Full Document Attached Below:

A detailed document titled “Random Forest and SVM” has been prepared by our team member Rosemary. It includes theoretical explanations, real-life applications, advantages, and examples for better understanding.

You can access the PDF file below for complete details.