Random Forest vs. Neural Network: What’s the Difference?

A random forest model and a neural network are different kinds of artificial intelligence (AI) that you can use for various purposes. Random forest is a machine learning algorithm that allows the computer, or model, to predict an outcome, such as predicting what category an object should be classified in or making a yes or no decision about a topic. 

A neural network, on the other hand, is a deep learning algorithm that allows a computer or AI model to interact with data in complex ways. Sometimes, you can use these models to create deep learning machines that can make predictions by considering a wide range of variables. Explore both of these techniques, including how they work, how you can use them, and how they differ. 

What is a random forest model? 

A random forest is a machine learning algorithm with an ensemble of decision trees that you can use for tasks like classification or regression. These algorithms can predict the most likely answer to the task you’ve given them. For example, if you wanted to classify images, a random forest could make a prediction about whether the image contains a cat or a dog. 

A random forest contains a large group of decision trees that each make a prediction about the input. Each decision tree within a random forest model might have slightly different questions at each branch, allowing the algorithms to come to other decisions. This technique can help account for all of the variables that might change the correct answer.

How does a random forest work?

Before you can fully grasp the functions of a random forest algorithm, it helps to understand how a decision tree works. A decision tree is an algorithm that starts with an input and asks branching yes or no questions to broaden its understanding and make a decision based on the data. You might be familiar with the concept if you can picture an infographic or similar visual that asks you a yes or no question at every step of the graph until ultimately directing you to the best choice for your situation. 

In a random forest algorithm, you can use bagging, which allows for some feature randomness. This process will enable the decision trees to ask slightly different questions. This is similar to asking a crowd of people to make a decision, as each person will use slightly different factors—and weigh those factors differently—to come to a decision. However, you can make a more informed decision by bringing more perspectives, which can help you compare all points. That’s very similar to how a random forest works. 

What is random forest used for? 

You can use a random forest in various applications to make predictions and decisions. A few examples of specific use cases for these algorithms from the industries of health care, finance, and retail include: 

• Predicting fraud: Financial companies can use random forest algorithms to spot potentially fraudulent transactions. 

• Health care: In the medical industry, researchers can use random forest models for computational research, such as sequencing and classifying gene expression, and predicting disease progression and patient outcomes. 

• Retail: Marketing professionals can use random forest algorithms to create recommendation apps that connect customers with products they would like and predict other kinds of customer behavior. 

What is a neural network? 

A neural network is a deep learning AI model that allows a computer to think mechanically similar to humans. Neural networks also allow computers and AI models to recognize patterns and decide the best course of action to accomplish a task by weighing all the available options and learning from past mistakes. 

How does a neural network work?

A neural network contains layers of interconnected nodes, each able to manipulate or interact with the input somehow. Each node also includes a weight signifying how vital that data component is to the final output. This is similar to how the human brain's neurons work together to manipulate data in complex ways. When you add more layers of nodes, you can increase the complexity of how the algorithm can interact with your input. 

To train a neural network, you can choose different strategies for setting the weights to start and then fine-tune those settings using training data to determine the optimum balance. These weights help the algorithm understand what information is most important to finding a solution for the output and which variables are less important or should be ignored. 

For example, if you wanted to decide whether it was a good day for a picnic, you might consider a couple of factors. You might determine if you have plans for the day, whether you have picnic supplies, and whether the weather is nice enough to go. If you have no plans but need to pick up a few items from the store before you go, you may decide that the answer is yes, it’s a good day for a picnic. But if you see a thunderstorm outside, you might weigh that variable as the most important and decide not to go. The weights of a neural network allow the AI model to make decisions similarly. 

What are neural networks used for? 

Neural networks are an important foundational technology that makes other technologies possible, including computer vision and natural language processing. 

• Computer vision: You can use convolutional neural networks to analyze and understand images, which makes it possible for AI models to perform face recognition, drive vehicles, and identify cancer in medical imagery, to name a few examples. 

• Natural language processing: Neural networks can understand and process text and written language, allowing AI models to speak naturally to us, understand our verbal and written commands, and perform tasks like translation. 

Types of neural networks 

You can choose the type of neural network to use according to the task you’d like the AI model to help you with. The three most common types of neural networks include: 

• Feedforward: In this type of neural network, information “feeds forward” from the first input layer through each layer to the last output layer without moving in loops.

• Recurrent: In a recurrent neural network, information can move in loops. This allows the AI model to remember previous details and work with time series data. 

• Convolutional: A convolutional neural network differs from the others because it has a convolutional layer that enables the model to extract features in an image. 

Random forest vs. neural network: Other things to consider 

Each model—random forest and neural network—has strengths and weaknesses. Random forest, for example, is good at predicting how to classify items, handling large data sets, and generalizing to data it’s never seen before. Random forest models can also help data scientists consider approaching a problem. At the same time, they can be slow to train and run, and it can be more challenging to understand precisely how the algorithm came to make the prediction that it did. A random forest can also work with tabular data only, which puts it at a disadvantage compared to a neural network, which can work with many data formats. 

A neural network is a flexible model that can work with incomplete data sets. Still, it can be prone to under- and overfitting, where the model is trained too much or too little and can’t adequately function when faced with data it has never seen before. One tactic you can use to overcome these challenges is to combine them. A random forest can help prevent over- or underfitting because the feature randomness allows the algorithm to come to a more accurate prediction, particularly in use cases where you have a very small amount of data to work with.

Explore random forest models and neural networks on Coursera

Random forest models and neural networks are different types of artificial intelligence: random forest models are machine learning algorithms used to make predictions, and neural networks are deep learning models that can work with data in complex ways.

To learn more about either of these topics, consider a Specialization to help you learn new skills and learn to use AI in your career. For example, you could enroll in the Machine Learning Specialization offered by Deep Learning.AI and Stanford University, where you’ll master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng.