In supervised machine learning, selecting the right classifier is critical to how well the model will perform. Decision Trees and Random Forests are probably the two most popular algorithms, to start, in general, because of how simple, powerful and flexible they are.
| TL;DR | |
What are they? | Machine learning models used for classification and regression. |
Decision Tree | Works like a flow chart that is very understandable and performs well for simple problems, but can overfit. |
Random Forest | Combines many trees for better accuracy and stability, and is less interpretable. |
How do they work? | A decision tree traverses a single path of decisions. A random forest takes the average of many trees. |
Speed | Trees train quickly. Forests take longer to train, but they are reliable. |
Accuracy | Random forests have an average accuracy comparable to trees, but increase accuracy when the data is more complex. |
Best used when | Decision trees are good for getting quick and interpretable answers. Random forests should be used when accuracy is more important. |
Common issues | Deep decision trees can overfit data. Random forests can be viewed and tasted like a black box of complexity. |
In this article, you will learn how each model works, what use case they work best in, and what criteria to use to make the right choice between a Decision Tree and Random Forest in terms of accuracy, complexity, and interpretability. So, let’s dive in!
What is a Decision Tree? Read Below
A Decision Tree is an algorithm used for machine learning that utilizes a tree-like model and makes decisions based on some input or data. It operates by splitting categories of the data by rules, which are divided and partitioned from the root to the end of the leaves.
Each internal node is a condition, or test, on a feature, while each end node is a final prediction or outcome. The reader can follow the decision process for the model follows the rules from the beginning (root) to the end (leaves), which makes it intuitive.
Key Characteristics:
- Interpretability. You can easily follow the decisions made by the model.
- Training time. It can be built very quickly and with little computational power.
- Overfitting. If left alone, the tree can create a large and complex structure that merely memorizes the training data.
What is a Random Forest? read Below
A Random Forest is a type of ensemble learning method that generates predictions from multiple Decision Trees to improve accuracy and reduce overfitting. Each tree is trained on a unique random portion of the data through a process called bootstrapping. When it is time to make predictions, the Random Forest collects the predictions from all of the constituent trees, which is most commonly done by majority vote, if classifying.
By combining the predictions of many models, Random Forest averages out errors, creating a more robust learning algorithm than a single Decision Tree.
Key Traits of Random Forest:
- An ensemble of Decision Trees: This is the ensemble method that combines many weak learners together.
- Uses bootstrapping and averaging instead of one trained decision tree: Each tree in the ensemble sees a different view of the data, and then predictions are averaged.
- Reduces overfitting: More stable and generalizable than any one decision tree.
- More accurate, less interpretable: It is harder for the user to understand how the model decides stuff.
Key Differences: Decision Tree vs Random Forest
While Decision Trees and Random Forests serve the same classification and regression purposes, they differ in their operation and performance. A Decision Tree is an individual model that determines decisions based on rules in the form of a sequence of features. A Random Forest is a collective of multiple Decision Trees that collaborate to provide a more accurate and stable result.
Here for a comparison based on key aspects are the most substantial aspects:
Feature | Decision Tree | Random Forest |
Complexity | Low | High |
Accuracy | Medium | High |
Overfitting | High | Low |
Interpretability | Easy | Difficult |
Training Time | Fast | Slower |
Tip: Use Random Forest when you care more about accuracy than interpretability. Random Forests are particularly beneficial for complex datasets where a single Decision Tree may not perform.
Accuracy Comparison Using Scikit-learn
If you want to see how Decision Trees and Random Forests perform on a small dataset, use Scikit-learn and follow the steps below:
- Load a sample dataset: Load a dataset that is already built in, such as Iris or Titanic. These datasets are good to use for quick checks and model comparisons.
- Train each model: Fit a Decision Tree classifier and a Random Forest classifier on the same training dataset using Scikit-learn.
- Evaluate performance: After creating a prediction for each model on the test set, you can compare the two models with their accuracy, precision, and recall scores. All of these metrics will measure how well each model classified the dataset.
- Visual comparison: You can use a confusion matrix to see which classes the model confused, or use an ROC curve plot if you are classifying for a binary classification problem. Visuals are good for easy explanation of your model choice and justification.
In most cases, Random Forest will have better accuracy and more consistent metrics than Decision Tree. Heuristics blinds me from saying it will not always be perfect, but a Decision Tree will probably not be able to generalise very well if it does so on a noisy or complicated dataset.
When to Use Each Model:
Decision Trees and Random Forests both have a purpose, but the right decision depends on your problem. Knowing when to use each model can help you save time and also come closer to the right solution.
Use a Decision Tree when:
- You have a small and simple dataset.
- You want to articulate and describe the logic of the model to your non-technical stakeholders.
- You care more about transparency and interpretability than you do about hitting perfect accuracy.
- You want the model trained and fast answers!
Use Random Forest when:
- You want overall higher accuracy.
- You have too many features and/or your dataset is noisy.
- You are worried about overfitting.
- You are solving a real-world problem, and the generalization error describes the problem better than the interpretive accuracy of the model.
These models are generally complementary and serve different functions. Decision Trees are great models for clarity and speed, whereas Random Forests are preferable when you want great and reliable predictions.
Best Practices for Assignments
When performing assignments using Decision Trees or Random Forests, you will need to do more than run the methods to obtain your results. A thoughtful approach will not only improve your results but will also help you stand out.
- Always begin with EDA and preprocessing: The first step is to explore your dataset. Is there missing data? Are there outliers? Are the classes balanced? EDA and preprocessing occur before you train any model.
- Cross-validate models: Do not rely on a single train-test split. Rather, use K-Fold cross-validation to obtain a better estimate of your model’s performance.
- Show confusion matrices and precision/recall: Accuracy is not enough. Prepare a summary of precision, recall, and F1-score to showcase how well your model works, especially with imbalanced data.
- Use GridSearchCV to do hyperparameter tuning: There is more than one tuning parameter for both Decision Trees and Random Forests (max_depth for Decision Trees, and n_estimators for Random Forests, for instance). You can perform fine-tune search combinations with GridSearchCV.
Tools & Libraries Used
When developing and assessing models such as Decision Trees and Random Forests, a few essential Python libraries are required. These instrumentations facilitate data experiences, model development, employment, and visualization of results.
- Pandas, NumPy – Data Experiences: Pandas is used to load and manage data sets, while NumPy handles fast numerical capabilities in the background. Pandas and NumPy are the foundation for most ML projects.
- Scikit-learn – Models: Scikit-learn has usable implementations of DecisionTreeClassifier and RandomForestClassifier, and more tools for slicing data, hyperparameter tuning, and assessing model performance.
- Matplotlib, Seaborn – Visualization
These libraries are well-suited for producing simple plots as an effective communication tool. You can create feature distribution plots, confusion matrices, and ROC curves, and visualize the graph structure of a Decision Tree.
The tools are well supported by their community, work for beginners, and are widely used in academia and industry projects.
Conclusion
The decision of whether to use Decision Trees or Random Forests is not about deciding which one is the better algorithm in absolute terms; it is about deciding which one is best in your context. Sometimes you need simplicity and speed, while other times you need accuracy and robustness.
Knowing which is which will help you to make good modeling decisions, especially when you are working on coursework, practical exercises, or interview questions, when the justification of your choice is important.
Key Takeaways:
- Decision Trees are fast, generally understandable, and good for simple or interpretable tasks.
- Random Forests are likely more accurate on the whole, and they are more robust and likely to be a better choice for tasks in large or noisy datasets.
- Choose Decision Trees when the explainability of the modeling choice is important.
- Choose Random Forests when your main priorities are accuracy and better generalization.
