Difference Between Random Forest vs Decision Tree

Algorithms are required for the execution of powerful computer programs. The faster the algorithm executes, the more efficient it is. Algorithms are created using mathematical principles to work through AI and Machine Learning problems;  Random forest and decision tree are two such algorithms. These algorithms assist in handling q vast amounts of data to make better evaluations and judgments. 

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Let’s begin with understanding the meaning of Decision Tree and Random Forest. 

Decision Tree

As the name implies, this approach constructs its model in the form of a tree, complete with decision nodes and leaf nodes. Decision nodes are arranged in the order of two or more branches, with the leaf node representing a decision. A decision tree is a simple and efficient decision-making flowchart implemented to manage classified and consistent data. 

 Trees are a simple and convenient approach to viewing algorithm outcomes and learning how decisions are produced. A decision tree’s key advantage is adjusting according to the data. A tree diagram can be used to see and analyze the process results in an organized manner. On the other hand, the random forest approach is considerably less likely to be affected by aberrations because it generates several separate decision trees and averages these forecasts.

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Advantages of Decision Tree

  • Decision trees demand less time for data preprocessing than other methods.
  • A decision tree doesn’t involve regularization.
  • A decision tree does not necessitate data scalability.
  • Discrepancies in the data do not significantly impact the decision tree’s development process.
  • A decision tree paradigm is very natural and simple to communicate to technical teams and stakeholders.

Disadvantages of Decision tree

  • A minor change in the data can significantly change the decision tree data structure, resulting in destabilization.
  • A Decision tree’s computation can be significantly more complex than other algorithms at times.
  • The training period for a decision tree is frequently longer.
  • Decision tree education is costly due to the increased complexity and time required.
  • The Decision Tree technique is insufficient for performing regression and forecasting continuous variables.

Random forest

The Random forest has nearly identical hyper-parameters to a decision tree. Its decision tree ensemble approach is produced from randomly divided data. This entire community is a forest, with each tree containing a unique random sample.

Many trees in the random forest technique can make it too slow and inefficient for real-time prediction. In contrast, the random forest method generates results based on randomly picked observations and characteristics constructed on multiple decision trees.

Since random forests only use a few variables to generate each decision tree, the ultimate decision trees are typically decorrelated, implying that the random forest methodology model is difficult to surpass the database. As previously stated, decision trees typically overwrite the training data, implying as more likely to fit the dataset’s clutter than the genuine underlying system.

Advantages of Random forest

  • Random forest is capable of performing both classification and regression problems.
  • A random forest generates easy-to-understand and precise forecasts.
  • It is capable of effectively handling massive datasets.
  • The random forest method outperforms the decision tree algorithm regarding prediction accuracy.

Disadvantages of Random forest 

  • Additional compute resources are required when using a random forest algorithm.
  • It is more time-consuming than a decision tree. 

Difference between Random Forest and Decision Tree

Data processing: 

The decision trees use an algorithm to decide on nodes and sub-nodes. A node can be divided into two or more sub-nodes, and generating sub-nodes gives another cohesive sub-node, so we can say that the nodes have been divided. 

The random forest, on the other hand, is the combination of various decision trees, which is the class of the dataset. Some decision trees may give an accurate output while others may not, but all trees make predictions together. The split is initially carried out using the best data, and the operation is repeated until all child nodes have reliable data.


The decision tree, which is used for classification and regression, is a straightforward series of choices taken to obtain the desired outcomes. The benefit of the simple decision tree is that this model is easy to interpret, and when building decision trees, we are aware of the variable and its value used to split the data. As a result, the output can be predicted quickly. 

In contrast, the random forest is more complex because it combines decision trees, and when building a random forest, we have to define the number of trees we want to make and how many variables we need.


When compared to decision trees, random forest forecasts outcomes more accurately. We can also assume that random forests build up many decision trees that merge to give a precise and stable result. When we use an algorithm for solving the regression problem in a random forest, there is a method to get an accurate result for each node. The method is known as the supervised learning algorithm in machine learning, which uses the bagging method.


When using algorithms, there is a risk of overfitting, which can be viewed as a generalized constraint in machine learning. Overfitting is a critical issue in machine learning. When machine learning models cannot perform well on unknown datasets, it is a sign of overfitting. This is especially true if the problem is detected on the testing or validation datasets and is significantly larger than the mistake on the training dataset. Overfitting occurs when models learn fluctuation data in the training data, which harms the performance of the new data model.

Due to the employment of several decision trees in the random forest, the danger of overfitting is lower than that of the decision tree. The accuracy increases when we employ a decision tree model on a given dataset since it contains more splits, making it easier to overfit and validate the data.

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End Note

A decision tree is a structure that employs the branching approach to show every conceivable decision outcome. In contrast, a random forest is a collection of decision trees that produces the final result depending on the results of all of its decision trees.

Learn more about Random Forest and Decision Tree 

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Is a decision tree preferable over a random forest?

Multiple single trees, each based on a random training data sample, make up random forests. Compared to single decision trees, they are often more accurate. The decision boundary gets more precise and stable as more trees are added.

Can you create a random forest without using decision trees?

By using feature randomness and bootstrapping, random forests can produce decision trees that are not correlated. By choosing features at random for each decision tree in a random forest, feature randomness is obtained. The max features parameter allows you to regulate the amount of features used for each tree in a random forest.

What is a decision tree's limitation?

The decision trees' relative instability compared to other decision predictors is one of their drawbacks. A minor change in the data can significantly impact the decision tree's structure, transmitting a different outcome than what users would typically receive.

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