Home /Difference Between Random Forest and Decision Tree in Machine Learning
Algorithms play a critical role in executing any computer program effectively. The faster an algorithm operates, the quicker the program will execute. Machine learning offers various algorithms for regression and classification tasks, but two of the most widely used ones are decision trees and random forests. Although these algorithms have similarities, they also have differences that set them apart. This blog post will examine the distinctions between decision trees and random forests.
Early artificial intelligence pioneer Arthur Samuel introduced Decision Trees in the 1950s. These trees are closely associated with game theory and were created to address decision-making challenges in the military strategy and economics domains.
The Decision Tree algorithm is a widely-used supervised learning technique in machine learning for classification and regression tasks. The algorithm repeatedly divides the dataset into smaller subsets based on the most informative feature to increase class separation or minimize variance. Each split results in a new branch in the tree, and the outcome is a tree structure where each leaf node represents a class or a value.
Decision trees have the advantages of being interpretable, handling both categorical and continuous data, and working for binary and multi-class classification and regression tasks. However, overfitting can be a problem, especially if the tree is too deep, leading to a poor generalization of new data.
Let’s go through an easy example.
To use a decision tree to decide whether to run based on the weather and energy level, you would start by creating a root node labeled “Should I go for a run?” The first decision point would be based on the weather, with a branch for “Is it raining?” If it is raining, the decision would be not to go for a run. If it’s not raining, move to the next decision point based on an energy level with a branch for “Am I feeling energetic?” If you are feeling energetic, the decision would be to go for a run, and if not, the decision would be not to go for a run. The decision tree helps by considering weather and energy levels, with a clear set of conditions for each outcome.
In the early 2000s, Leo Breiman, a notable figure in machine learning, introduced the concept of Random Forests. Breiman was a pioneer in the field of machine learning.
Random Forest is an ensemble machine-learning algorithm that can be used for classification, regression, and other tasks. It combines multiple decision trees to form a forest, each trained on a random subset of the training data and a random subset of input features. During training, each tree learns to make predictions based on the features of the data points it sees. During prediction, each tree makes a prediction, and the final result is an average of individual tree predictions for regression or the majority vote for classification
Random Forest offers several benefits over decision trees, such as improved Accuracy, reduced overfitting, and better performance on high-dimensional data. It is scalable and suitable for large datasets with many input features. Random Forest is commonly used in finance, marketing, and healthcare, among other fields, and is among the most popular machine learning algorithms.
Let’s understand by an Example.
To better understand how the random forest algorithm works, consider an example where you plan to buy a new cycle. You have multiple options, such as cycles X, Y, and Z. To make an informed decision, you could build a separate decision tree for each cycle option and then combine the results to determine the best option overall. This approach involves building separate decision trees for each cycle option, similar to building a single decision tree, and then analyzing the output from each tree using a predictive system to find the best cycle option. Using a random forest algorithm, you can apply this approach across many options or scenarios to make more accurate and informed decisions.
The decision to use either a decision tree or a random forest depends on the specific problem and its requirements. If speed is important and Accuracy is not a top priority, a decision tree may be a good choice since it can be quickly built. However, if Accuracy is important and Time is not a constraint, a random forest may be preferred since it can produce better predictions but requires more Time for training. Decision trees are better suited for larger datasets as they are faster, while random forests require more training time for larger datasets. Ultimately, the choice between the two models should be based on the specific problem and its requirements.
After considering the strengths and weaknesses of each algorithm, it is important to analyze the problem and choose the most suitable algorithm. We can make a more informed decision by understanding the differences between decision trees and random forest algorithms. Implementing these algorithms in practice is recommended to gain a deeper understanding of these topics.