Random Forest vs Decision Tree: What's the Difference?

In the cutting-edge world of Machine Learning (ML), two powerful contenders are vying for the spotlight as the best algorithm: Random forest and Decision Tree. Each of them brings its unique charm. While the Decision Tree stands alone—simple, interpretable, and ideal for smaller datasets—random forest steps in like an ensemble when data complexity increases, combining multiple trees to create a more robust performance.

So, knowing when to use one over the other can make a radical difference in your Data Science journey. This blog explores the key differences between Random Forest Vs Decision Tree, to help you pinpoint the exact choice for your data analysis needs.

Table of Contents

1) What Is Random Forest?

2) What Are Decision Trees?

3) Differences Between Random Forest and Decision Trees

4) When to Use Random Forest Vs Decision Tree

5) Conclusion

What is Random Forest?

A random forest is a powerful tree-based algorithm in Machine Learning (ML). It works by creating several Decision Trees during the training phase using Training Data, where each tree is through a random subset of the dataset to measure a subset of features in each partition. This randomness introduces variability among individual trees, improving overall prediction performance and reducing the risk of overfitting.

What Are Decision Trees?

A Decision Tree is a supervised non-parametric learning algorithm utilised for classification and regression tasks. It has a hierarchical tree structure comprising branches, leaf nodes, a root node, and internal nodes. The Decision Tree commences with a root node (that has no incoming branches) and outgoing branches from the root node feed into internal nodes (also known as decision nodes).

Based on available features, both node types perform evaluation to form homogenous subsets, signified by terminal nodes or leaf nodes. The leaf nodes represent every possible outcome within the dataset.

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Differences Between Random Forest and Decision Trees

The following table summarises the key differences between random forest and Decision Tree models:

When to Use Random Forest Vs Decision Tree

The following points will help you decide when to use the random forest model and when to opt for a Decision Tree:

1) Go for a Decision Tree when interpretability is essential, and you require a simple, easy-to-understand model.

2) Use a random forest when you seek improved accuracy, better generalisation performance and robustness to overfitting, especially on complex datasets with high-dimensional feature spaces.

3) When computational efficiency is a priority and you're working with a small dataset, a Decision Tree is the way to go. Its simplicity and speed make it a good fit for these scenarios.

4) A random forest can provide better results if you have a vast dataset with complex relationships between labels and features.

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Conclusion

In conclusion, the choice between Random Forest Vs Decision Tree comes down to the dataset's complexity and your data analysis needs. Decision Trees are the ideal option for interpretability and simplicity involving smaller datasets. However, when greater accuracy and robustness involving large datasets are the priority, random forest is the go-to solution. The key distinctions between the two, as outlined in this blog, will help you make the right choice for your data science requirements.

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