Differences: Decision Tree & Random Forest

sample random forest

In machine learning, there are a few different algorithms that can be used for regression and classification tasks. Two of the most popular are decision trees and random forest. Both of these algorithms have their similarities and differences, and in this blog post, we’ll take a look at the key differences between them.

What is decision tree algorithm?

A decision tree is a machine learning algorithm that can be used for both classification and regression tasks. The algorithm works by splitting the data into smaller subsets, and then using these subsets to make predictions. Each split is based on a decision criterion, such as the purity of the data or the entropy of the data. The decision tree algorithm continues to split the data until it reaches a point where it can no longer improve the predictions. At this point, the tree is said to be “grown.” The decision tree algorithm can be used with both categorical and numerical data. It is a popular algorithm because it is relatively easy to understand and interpret, and it can be used with a variety of data types.

This is how a sample decision tree would look like:

sample decision tree

Learn more about decision tree in this post – Decision trees concepts and examples.

What is random forest algorithm?

Random Forest is a machine learning algorithm that can be used for both regression and classification tasks. It is an ensemble of decision trees, which means that it uses multiple trees to make predictions. Each tree in the Random Forest is trained on a random subset of the data, and the final prediction is made by averaging the predictions of all the trees. This approach has a number of advantages. First, it helps to prevent overfitting, since each tree only has access to a limited amount of data. Second, it makes the Random Forest more robust to outliers and errors in the training data. Finally, it makes the algorithm more efficient, since each tree only needs to be trained on a small subset of the data. As a result, Random Forest is a powerful and popular machine learning algorithm that can be used for a variety of tasks.

This is how a sample random forest would look like:

sample random forest

Learn more about random forest in this post: Random forest classifier python example

Key differences between decision tree and random forest

Random forest and decision tree are two popular methods used in machine learning. Both methods can be used for classification and regression tasks, but there are some key differences between them. – – – —-

  • Random forest is a ensemble learning method, which means it uses a combination of multiple models to make predictions. In contrast, decision tree is a single model that makes predictions based on a series of if-then rules. Recall that ensemble learning is a machine learning technique where multiple models are trained to solve a problem. The individual models are then combined to form a final model that is more accurate than any of the individual models. Ensemble learning is often used in situations where the individual models are not very accurate, but the ensemble model is able to achieve high accuracy by combining the predictions of the individual models.
  • Perhaps the most significant difference is in the objective function that each model uses. A decision tree is typically created using a greedy algorithm, which means that it focuses on finding the locally optimal solution at each step. In contrast, Random Forest creates a ensemble of decision trees, each of which is trained on a subset of the data. This allows Random Forest to find the globally optimal solution, rather than getting stuck in a local optimum. As a result, Random Forest tend to be more accurate than decision trees.
  • Random forest is less likely to overfit the data than decision tree. This is because each individual model in random forest is trained on a random subset of the data, which reduces the chance that the model will learn from noise rather than signal. Overfitting occurs when a model memorizes the training data too closely and does not generalize well to new data points. Random Forest alleviates this issue by creating multiple decision trees and averaged their predictions.
  • Random forest is generally more accurate than decision tree, but it is also more computationally expensive since it requires training multiple models. However, the extra computational cost can be offset by the improved accuracy of Random Forest.
  • Decision tree is faster and easier to train, but it is less flexible and can overfit the data if not tuned properly. 
  • Another key difference between the two models is that random forest models can handle missing values, whereas decision trees models cannot. Random Forest can deal with missing data by using bootstrapping, while decision tree typically relies on imputation. This means that Random Forest is more robust to missing data, but it can also be more computationally expensive. This makes random forest a more robust modeling approach overall.
  • Random forest strives to minimize the variance, while decision tree attempts to minimize the entropy.

Ultimately, the choice of model depends on the specific task and the available resources

Conclusion

Both decision trees and random forests are powerful machine learning algorithms that can be used for both regression and classification tasks. However, there are some key differences between them. Decision trees tend to overfit the training data, while random forests are much more resistant to overfitting. Additionally, decision trees require less data preprocessing than random forests, and they’re also easier to interpret because you can visualize the entire tree.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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