Decision Trees and Random Forests: Two Powerful Machine Learning Algorithms
We touched on the differences between Artificial Intelligence, Machine learning, and deep learning. Machine learning has changed industries by enabling computers to make data-driven decisions. Among the most widely used algorithms for classification and regression tasks (A problem that requires predicting a continuous numerical value, ex. Predicting the price of a house based on various factors) are Decision Trees and Random Forests. These two methods offer efficient and powerful solutions for various problems, from customer segmentation to fraud detection.
In this blog, we’ll explore how Decision Trees and Random Forests work, their advantages, differences, and real-world applications.
What is a Decision Tree?
A Decision Tree is a supervised learning algorithm used for both classification and regression tasks. It operates like a flowchart, where each node represents a decision based on a feature, leading to a final prediction at the leaf node.
How It Works:
The algorithm starts with the root node, containing the entire dataset.
The dataset is split into branches based on the feature that best separates the data.
This process continues, forming multiple levels of decisions, until reaching a leaf node, which represents the final prediction.
Example:
Imagine a bank wants to determine whether to approve or deny a loan application. A Decision Tree might use features like credit score, income, and employment status to classify applicants into "Approve" or "Deny."
If an applicant has a credit score above 700, the tree might immediately classify them as "Approve." If it's lower, further checks like income level might decide the final outcome.
Advantages of Decision Trees:
Easy to understand – Their tree-like structure makes them highly interpretable.
Handles both numerical and categorical data – Unlike some ML models, Decision Trees work with mixed data types.
Requires minimal data preparation – No need for feature scaling or complex preprocessing.
Limitations of Decision Trees:
Prone to overfitting – A tree that grows too deep captures noise rather than meaningful patterns.
Sensitive to small changes in data – A minor alteration in input data can lead to an entirely different tree structure.
To overcome these limitations, machine learning engineers use Random Forests, which improve Decision Trees by introducing ensemble learning.
What is a Random Forest?
A Random Forest is an ensemble learning algorithm that combines multiple Decision Trees to improve accuracy and robustness. Instead of relying on a single tree, it builds multiple trees and aggregates their predictions.
How It Works:
Bootstrap Sampling – The dataset is randomly sampled with replacement to create multiple training subsets.
Decision Tree Training – Each subset is used to train an independent Decision Tree.
Random Feature Selection – Each tree considers a random subset of features, ensuring diversity among trees.
Aggregation of Predictions:
For Classification: The final prediction is determined by majority voting across all trees.
For Regression: The final output is the average of all predictions.
Example:
Returning to the loan approval case, instead of using just one Decision Tree, a Random Forest would generate multiple Decision Trees, each analyzing a subset of applicant data. By aggregating their predictions, the Random Forest reduces the risk of overfitting and provides a more reliable decision.
Advantages of Random Forests:
More accurate than Decision Trees – Reduces overfitting by averaging multiple predictions.
Handles missing data well – Can still produce reasonable predictions when some data points are missing.
Works well with high-dimensional data – Suitable for datasets with many features.
Limitations of Random Forests:
Computationally expensive – Training multiple trees takes more time and resources.
Less interpretable than a single Decision Tree – Since it combines multiple models, understanding why a specific prediction was made can be difficult.
Key Differences Between Decision Trees and Random Forests
The decision tree approach is a single tree-based model while the random forest method is an ensemble of multiple trees. The ensemble of multiple trees allows for the random forest method to surpass the decision tree method in multiple areas such as accuracy and reducing overfitting. At the same time, the random forest is slower, more complex, and harder to interpret compared to the Decision tree method.
Real-World Applications of Decision Trees and Random Forests
1. Healthcare: Disease Diagnosis
Medical professionals use Decision Trees to diagnose diseases based on symptoms. For example, a tree can classify whether a patient has diabetes based on features like blood sugar level, age, and weight. Random Forests improve accuracy by combining multiple trees, reducing the risk of misdiagnosis.
2. Finance: Credit Scoring and Fraud Detection
Banks use Decision Trees to evaluate loan applications and predict whether a borrower is likely to default. Random Forests further enhance accuracy in fraud detection, identifying patterns in fraudulent transactions by analyzing spending behavior.
3. E-commerce: Customer Segmentation
Retailers use Decision Trees to classify customers based on their purchase behavior and personalize marketing strategies. Random Forests improve product recommendation engines, increasing customer engagement.
4. Manufacturing: Quality Control
Manufacturers use Decision Trees to detect defective products based on sensor data.
Random Forests improve efficiency by analyzing multiple factors to reduce errors.
Choosing Between Decision Trees and Random Forests
Use a Decision Tree when interpretability is important, and the dataset is relatively small.
Use a Random Forest when higher accuracy is required, and the dataset is large with many features.
For most practical applications, Random Forests outperform individual Decision Trees due to their ability to generalize better and handle noisy data.
Conclusion
Decision Trees and Random Forests are powerful machine learning algorithms that excel in classification and regression tasks. Decision Trees provide quick, interpretable results, while Random Forests improve accuracy and reduce overfitting by combining multiple trees.
Both methods are widely used in industries like healthcare, finance, retail, and manufacturing, proving their effectiveness in real-world applications.
As machine learning advances, ensemble methods like Random Forests will continue to be essential in building reliable AI models.