Introduction
When it comes to machine learning, choosing the proper model is crucial for students. A model that functions appropriately with one kind of data set might not work with another. Both Decision trees and Random Forests are strong machine-learning algorithms that involve both classification and regression.
Students who take up machine learning as their higher studies often have to complete assignments by having an understanding of both decision tree and random forest algorithms. While doing these assignments, they face challenges.
This blog is to show the difference between decision tree and random forest, overcome the challenges and gather an understanding of machine learning. It is suggested that you read this blog to learn about the world of machine learning. Let’s dive in quick!
Advantages of Decision Tree in Machine Learning
Decision Tree is a famous supervised machine learning algorithm which is utilised for classification and regression problems. This section focuses on the advantages of Decision Tree that students will get:
- It is easy to handle
- It has a transparent process
- It can manage both categorical and numerical data
- It can take larger data and give better results
- The results are delivered in a rapid manner
- It can generate rational rules
- It has the capability to perform classification without much computation
- It gives away a clear indication for prediction and classification
Disadvantages of Decision Tree
- It sometimes overfit
- The cutting-off process takes time
- It cannot guarantee optimization
- It gives complex calculations
- The deflection gets high sometimes
- It is more prone to errors when the focus is on classification problems
- Training in decision tree is expensive
Advantages of Random Forest
Random forest is also a well-supervised machine learning algorithm that enables ensemble learning to solve complex problems and enhance overall accuracy. This section focuses on the advantages and disadvantages of random forest.
- It is powerful and very accurate
- It does not need to normalize
- It can handle various features simultaneously
- It can perform both classification and regression
- It also offers good predictions in a simple format.
Disadvantages of Random Forest
- It is biassed to particular features sometimes
- It cannot be utilized for methods of linear
- Cannot handle high-dimensional data
- It can help in finding descriptions of the relationships in the data
- It can become ineffective for real-time predictions
Decision Tree vs Random Forest
| Property | Random Forest | Decision Tree |
| Nature | It ensembles several decision trees | Ensembles only one decision tree |
| Interpredibility | Due to its ensemble nature, it is less interpretable. | Highly interpretable |
| Overfitting | Because of the ensemble averaging, it is less towards overfitting | More drawn to overfitting in case of deep tress |
| Training time | Multiple trees are constructed, therefore training time needs more time and leads to less speed | It is after in comparison, because a single tree requires to be built as well as trained |
| Stability to change | It is more stable to change for it’s ensemble nature | Becomes sensitive when given variton of data |
| Predictive time | Several predictions, hence slower prediction speed and takes time in giving predictions | Gives faster prediction, because only can be given |
| Performance | Can work better on large datasets | Performs well both large and small database |
| Handling outliers | It is more robust to outliers | More susceptible to outliers |
| Feature essentiality | It does not offer feature scores directly, rather use ensembles for feature scores. | Offers feature score which are less trustworthy |
The above table shows the difference between a decision tree and a random forest. Therefore, if students take a look at this table and read further on random forests and decision trees, the assignment of machine learning will become easy for them.
When to Use Both Random Forest and Decision Tree
Now that you know the differences, advantages, and disadvantages of both random forest and decision tree, it is time to see when you can make use of both.
- You can use a decision tree when interpretability is essential. Therefore, use an easy and simple model to understand.
- You can use random forest when better generalization performance and improved accuracy are required on complex datasets with high-dimensional feature areas.
- If you are concerned about computational efficiency and have a small dataset, then it is wise to use a decision tree.
- On the other hand, if you have a large dataset with complicated relationships between labels and features, then using random forest will offer better results.
Challenges students face in assignments making
As mentioned at the beginning of this blog, students face challenges in making their machine learning assignments where understanding both Random Forest and Decision Tree is important.
The common challenges that students face are:
Clarity of the subject
The first challenge students face is not having proper clarity about the subject. Especially students who do not come from the same subject background. Therefore, in the learning stage, students take time to grasp the subject, which delays the start of the assignment.
Resources
The second challenge students face is not being able to find reliable resources. Every subject assignment needs research in one form or the other. Therefore, when students do not understand where to get reliable information, they get confused.
Time management
Students are given multiple assignments to finish and are required to take part in other academic activities. Some students also work part-time while studying. Therefore, maintaining a balance becomes a challenge between assignment-making and other tasks.
Deadlines
Students miss deadlines for many reasons, such as health issues, issues in the family, financial hindrances, lack of guidance, etc. These challenges make a student miss their deadlines. Therefore, all these challenges require assignment help service.
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Final Thoughts
Random Forest and Decision Tree in machine learning is a complex topic to understand. However, this blog will help students to understand how these two algorithms are different from each other. We have covered the advantages and disadvantages of both Decision Trees and Random Forests, as well as the differences between them.
This blog also covers the challenges students face in writing a machine learning assignment. We have also provided a solution, Assignment Fox, which is the best assignment service help platform for Australian students. To know more about us, visit our website!
