Difference Between Random Forest and Decision Tree

Choosing the appropriate model is crucial when it comes to machine learning. A model that functions properly with one kind of dataset might not function well with another. Both Random Forest and Decision Tree are strong algorithms for applications involving regression and classification. The aim of the article is to cover the distinction between decision trees and random forests.

What is Decision Tree?

Decision Tree is very popular supervised machine learning algorithm used for regression as well as classification problems. In decision tree, a flow-chart like structure is build where each internal nodes denotes the features, rules are denoted using the branches and the leaves denotes the final result of the algorithm.

What is Random Forest?

Random Forest is very powerful supervised machine learning algorithm, used for classification and regression task. Random Forest uses ensemble learning (combining multiple models/classifiers to solve a complex problem and to improve the overall accuracy score of the model). In Random Forest multiple decision tree are built by considering the different subset of the given data and the average of all those to increase the overall accuracy of the model. As the number of decision tree in random forest increases the accuracy increases and overfitting also reduces.

Random Forest Vs Decision Tree

Property	Random Forest	Decision Tree
Nature	Ensemble of multiple decision trees	Single Decision Tree
Interpretability	Less interpretable due to ensemble nature.	Highly interpretable.
Overfitting	Due to ensemble averaging it is less prone to overfitting.	More prone to overfitting specially in case of deep trees.
Training Time	Since multiple trees are constructed, training time becomes more, and training speed becomes less.	A single tree needs to be built and trained, hence faster in comparison.
Stability to change	Since overall average is taken due to ensemble, it is more stable to change.	It becomes quite sensitive to variation in data.
Predictive Time	Multiple predictions, hence longer prediction time and slower prediction speed.	Faster prediction as compared to random forest, since a single prediction is made.
Performance	Generally performs well on large datasets.	It can perform well on small and large dataset as well.
Handling Outliers	Due to ensemble averaging more robust to outliers.	It is more susceptible to outliers.
Feature Importance	Do not provide feature score directly rather uses ensemble to decide feature score.	Provide feature score directly which are less reliable.

When to Use Random Forest vs. Decision Tree?

• Use a decision tree when interpretability is important, and you need a simple and easy-to-understand model.
• Use a random forest when you want better generalization performance, robustness to overfitting, and improved accuracy, especially on complex datasets with high-dimensional feature spaces.
• If computational efficiency is a concern and you have a small dataset, a decision tree might be more appropriate.
• If you have a large dataset with complex relationships between features and labels, a random forest is likely to provide better results.