What is Random Forest?

What Is Random Forest?

Random Forest is one of the most widely used and reliable machine learning algorithms in practice. It works by building hundreds or thousands of decision trees, each trained on a slightly different random subset of the data, and then combining their predictions to produce a final answer. For classification tasks, the forest takes a majority vote among the trees. For prediction tasks, it averages their outputs.

Imagine asking a hundred knowledgeable people the same question, where each person has access to slightly different information. The majority opinion of this group will almost always be more reliable than any single person's answer. Random Forest applies this same logic to machine learning.

How Random Forest Works

The algorithm follows a straightforward process:

1. Create random subsets -- From your training data, randomly sample many different subsets (with replacement, meaning some data points may appear multiple times)
2. Build decision trees -- Train a separate decision tree on each subset. At each split in the tree, only consider a random selection of features rather than all available features
3. Combine predictions -- When making a prediction, run the input through every tree in the forest and aggregate the results

The dual randomness -- random data subsets and random feature subsets -- ensures that each tree is different. This diversity is what makes the ensemble so powerful.

Why Random Forest Is So Popular

Several characteristics make Random Forest a go-to choice for businesses:

• Minimal tuning required -- Unlike many ML algorithms, Random Forest performs well with minimal configuration. Default settings often produce strong results.
• Handles messy data -- The algorithm is robust to missing values, outliers, and mixed data types (numbers and categories together).
• Resistant to overfitting -- While individual decision trees tend to memorize training data, the averaging effect across hundreds of trees produces models that generalize well to new data.
• Feature importance -- Random Forest naturally ranks which input variables are most important for predictions, providing valuable business insights.

Business Applications in Southeast Asia

Random Forest is deployed across diverse industries:

• Banking and finance -- Credit risk assessment at banks in Singapore and Indonesia. Random Forest models evaluate loan applications by considering dozens of factors simultaneously, providing reliable risk scores.
• Retail -- Customer segmentation and churn prediction for e-commerce platforms across ASEAN. The algorithm identifies which customer characteristics most strongly predict purchasing behavior.
• Healthcare -- Disease risk prediction in hospitals across Thailand and the Philippines. Random Forest models analyze patient data to flag individuals at high risk for conditions like diabetes or heart disease.
• Supply chain -- Demand forecasting and inventory optimization for manufacturers in Vietnam and Malaysia. The algorithm handles the complex, multi-factor nature of supply chain data effectively.

Random Forest vs. Other Algorithms

• vs. Single Decision Trees -- Random Forest is dramatically more accurate because it eliminates the tendency of individual trees to overfit. The trade-off is reduced interpretability.
• vs. Gradient Boosting (XGBoost) -- Gradient Boosting often achieves slightly higher accuracy but requires more careful tuning and is more prone to overfitting. Random Forest is the safer, easier choice for many applications.
• vs. Neural Networks -- For structured business data (spreadsheets, databases), Random Forest frequently matches or outperforms neural networks while being faster to train and easier to deploy.

Getting Started

Random Forest is an excellent first algorithm for businesses beginning their ML journey:

• Available in every major ML library and cloud platform
• Works well with the structured data most businesses already have (sales records, customer databases, transaction logs)
• Provides interpretable feature importance rankings that business stakeholders can understand
• Requires minimal data preprocessing compared to many other algorithms

The Bottom Line

Random Forest remains one of the best starting points for business ML applications because it delivers strong accuracy with minimal effort. For companies in Southeast Asia building their first predictive models, Random Forest offers a reliable, low-risk path to measurable results. Its ability to handle messy, real-world data and provide feature importance insights makes it particularly valuable for organizations still maturing their data practices.