View All
View All
View All
View All
View All
View All
View All
View All
View All
View All
View All
View All
View All

Random Forest Vs Decision Tree: Exploring Key Differences

By Pavan Vadapalli

Updated on Apr 01, 2025 | 10 min read | 53.3k views

Share:

Join the AI and ML Courses from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Programs in ML & AI to fast-track your career.

What is Decision Tree 

Decision Tree is a supervised learning algorithm used in machine learning. It operated in both classification and regression algorithms. As the name suggests, it is like a tree with nodes. The branches depend on the number of criteria. It splits data into branches like these till it achieves a threshold unit. A decision tree has root nodes, children nodes, and leaf nodes.

Recursion is used for traversing through the nodes. You need no other algorithm. It handles data accurately and works best for a linear pattern. It handles large data easily and takes less time.

Source

Decision Tree is a supervised learning algorithm used in machine learning. It operated in both classification and regression algorithms. As the name suggests, it is like a tree with nodes. The branches depend on the number of criteria. It splits data into branches like these till it achieves a threshold unit. A decision tree has root nodes, children nodes, and leaf nodes.

Recursion is used for traversing through the nodes. You need no other algorithm. It handles data accurately and works best for a linear pattern. It handles large data easily and takes less time.

How does it work?

1. Splitting

Data, when provided to the decision tree, undergoes splitting into various categories under branches. 

Must Read: Naive Bayes Classifier: Pros & Cons, Applications & Types Explained

2. Pruning

Pruning is shredding of those branches furthermore. It works as a classification to subsidize the data in a better way. Like, the same way we say pruning of excess parts, it works the same. The leaf node is reached, and pruning ends. It’s a very important part of decision trees.

3. Selection of trees

Now, you have to choose the best tree that can work with your data smoothly.

Here are the factors that need to be considered: 

4. Entropy 

To check the homogeneity of trees, entropy needs to be inferred. If the entropy is zero, it’s homogenous; else not.

5. Knowledge gain

Once the entropy is decreased, the information is gained. This information helps to split the branches further.

  • You need to calculate the entropy.
  • Split the data on the basis of different criteria
  • Choose the best information.

Tree depth is an important aspect. The depth informs us of the number of decisions one needs to make before we come up with a conclusion. Shallow depth trees perform better with decision tree algorithms. 

Must Read: Free nlp online course!

Advantages and Disadvantages of Decision Tree

The list mentioned below highlights the major strengths and weaknesses of decision tree.

Advantages

  1. Easy
  2. Transparent process
  3. Handle both numerical and categorical data
  4. Larger the data, the better the result
  5. Speed 
  6. Can generate understandable rules.
  7. Has the ability to perform classification without the need for much computation.
  8. Gives a clear indication of the most important fields for classification or prediction.

Disadvantages

  1. May overfit
  2. Pruning process large
  3. Optimization unguaranteed
  4. Complex calculations
  5. Deflection high
  6. Can be less appropriate for estimation tasks, especially in cases where the ultimate aim is to determine a continuous attribute’s value. 
  7. Are more prone to errors in classification problems 
  8. Can be computationally expensive to train. 

Checkout: Machine Learning Models Explained

2. Random Forest

Source

What is Random Forest?

Random Forest is yet another very popular supervised machine learning algorithm that is used in classification and regression problems. One of the main features of this algorithm is that it can handle a dataset that contains continuous variables, in the case of regression. Simultaneously, it can also handle datasets containing categorical variables, in the case of classification. This in turn helps to deliver better results for classification problems. 

It is also used for supervised learning but is very powerful. It is very widely used. The basic difference being it does not rely on a singular decision. It assembles randomized decisions based on several decisions and makes the final decision based on the majority.

It does not search for the best prediction. Instead, it makes multiple random predictions. Thus, more diversity is attached, and prediction becomes much smoother.

Placement Assistance

Executive PG Program13 Months
View Program
background

Liverpool John Moores University

Master of Science in Machine Learning & AI

Dual Credentials

Master's Degree19 Months
View Program

You can infer Random forest to be a collection of multiple decision trees!

How does Random Forest work?

Bagging is the process of establishing random forests while decisions work parallelly.

1. Bagging

  • Take some training data set
  • Make a decision tree
  • Repeat the process for a definite period
  • Now take the major vote. The one that wins is your decision to take.

2. Bootstrapping

Bootstrapping is randomly choosing samples from training data. This is a random procedure that promotes diversity. 

STEP by STEP

  • Random choose conditions
  • Calculate the root node
  • Split the data
  • Repeat the process
  • You get a forest of decision trees

Read : Naive Bayes Explained

Advantages and Disadvantages of Random Forest

Advantages

  1. Powerful and highly accurate
  2. No need to normalize
  3. Can handle several features at once
  4. Run trees in parallel ways
  5. Can perform both regression and classification tasks.
  6. Produces good predictions that are  easily understandable.

Disadvantages

  1. They are biased to certain features sometimes
  2. Slow- One of the major disadvantages of random forest is that due to the presence of a large number of trees, the algorithm can become quite slow and ineffective for real-time predictions. 
  3. Can not be used for linear methods
  4. Worse for high dimensional data
  5. Since the random forest is a predictive modeling tool and not a descriptive one, it would be better to opt for other methods, especially if you are trying to find out the description of the relationships in your data. 

Difference between random forest and decision tree:

Factor Decision Tree Random Forest
Basic Structure Single tree Ensemble of multiple trees
Training Typically faster Slower due to training multiple trees
Bias-Variance Tradeoff Prone to overfitting Reduces overfitting by averaging predictions
Performance Can suffer from high variance More robust due to averaging predictions
Prediction Speed Faster Slower due to multiple predictions
Interpretability Easier to interpret More difficult to interpret due to complexity
Handling Outliers Sensitive (can overfit) Less sensitive due to averaging
Feature Importance Can rank features Can rank features based on importance
Data Requirements Works well with small to moderate datasets Can handle large datasets better
Parallelization Not easily parallelizable Easily parallelizable training
Application Often used as a base model Often used when higher accuracy is required

What are some of the important features of Random Forest?

Now that you have a basic understanding of the difference between random forest decision tree, let’s take a look at some of the important features of random forest that sets it apart. The following random forest decision tree list will also highlight some of the advantages of random forest over decision tree. 

  • Diversity-  Each tree is different, and does not consider all the features. This means that not all features and attributes are considered while making an individual tree. 
  • Parallelization – You get to make full use of the CPU to build random forests. The reason behind this being each tree is created out of different data and attributes, independently. 
  • Stability- Random forest ensures full stability since the result is based on majority voting or averaging. 
  • Train-test Split- Last but not least, yet another important feature of random forest is that you don’t have to separate the data for train and test since 30% of the data unseen by the decision tree is always available. 

When exploring random forest vs decision tree python implementations, decision trees offer simplicity and quick setup, while random forests enhance accuracy and robustness by averaging multiple trees.

For a clear random forest vs decision tree example, consider a classification task: a decision tree might quickly classify data but risks overfitting, while a random forest combines multiple trees to improve accuracy and reduce overfitting.

If you are interested in machine learning and AI applications, you might also want to make a chatbot using Python, which leverages similar concepts of classification and decision-making models in AI.

Conclusion

Decision trees are very easy as compared to the random forest. A decision tree combines some decisions, whereas a random forest combines several decision trees. Thus, it is a long process, yet slow.

Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one. The random forest model needs rigorous training. When you are trying to put up a project, you might need more than one model. Thus, a large number of random forests, more the time. 

It depends on your requirements. If you have less time to work on a model, you are bound to choose a decision tree. However, stability and reliable predictions are in the basket of random forests. 

If you have the passion and want to learn more about artificial intelligence, you can take up IIIT-B & upGrad’s PG Diploma in Machine Learning and Deep Learning that offers 400+ hours of learning, practical sessions, job assistance, and much more.

Frequently Asked Questions (FAQs)

1. How is random forest different from a normal decision tree?

2. What are the main advantages of using a random forest versus a single decision tree?

3. What is a limitation of decision trees?

4. What are the advantages of random forest over single decision tree?

5. Does random forest always outperform decision tree?

Pavan Vadapalli

899 articles published

Get Free Consultation

+91

By submitting, I accept the T&C and
Privacy Policy

India’s #1 Tech University

Executive Program in Generative AI for Leaders

76%

seats filled

View Program

Top Resources

Recommended Programs

LJMU

Liverpool John Moores University

Master of Science in Machine Learning & AI

Dual Credentials

Master's Degree

19 Months

View Program
IIITB

IIIT Bangalore

Post Graduate Certificate in Machine Learning & NLP (Executive)

Career Essentials Soft Skills Program

Certification

8 Months

View Program
IIITB
bestseller

IIIT Bangalore

Executive Diploma in Machine Learning and AI

Placement Assistance

Executive PG Program

13 Months

View Program