Random Forest Vs Decision Tree: Exploring Key Differences
Updated on Apr 01, 2025 | 10 min read | 53.3k views
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Updated on Apr 01, 2025 | 10 min read | 53.3k views
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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.
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.
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.
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.
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The list mentioned below highlights the major strengths and weaknesses of decision tree.
Checkout: Machine Learning Models Explained
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.
You can infer Random forest to be a collection of multiple decision trees!
Bagging is the process of establishing random forests while decisions work parallelly.
Bootstrapping is randomly choosing samples from training data. This is a random procedure that promotes diversity.
STEP by STEP
Read : Naive Bayes Explained
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.
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.
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.
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