📌 What Are Decision Trees?
A Decision Tree is a flowchart-like structure in machine learning that splits data into branches based on feature values. It works by recursively partitioning the dataset into subsets, making decisions at each node until reaching a leaf node that represents the final prediction.
Pros
- Easy to visualize and interpret 📊
- Requires little data preprocessing 🧹
- Handles both numerical and categorical data 🔄
Cons
- Prone to overfitting 🚫
- Unstable (small data changes can lead to significant tree variations) 🔄
📌 What Are Random Forests?
Random Forests are an ensemble method that combines multiple decision trees to improve accuracy and reduce overfitting. It introduces randomness by selecting subsets of features and data for each tree, then aggregates predictions through voting or averaging.
Pros
- High accuracy and robustness 📈
- Handles high-dimensional data 📊
- Automatically mitigates overfitting via bagging 🚫
Cons
- Less interpretable than single decision trees 📚
- Computationally intensive 🧮
🧩 Key Differences
| Feature | Decision Tree | Random Forests |
|---|---|---|
| Model Type | Single tree | Ensemble of trees |
| Bias-Variance Tradeoff | High variance, low bias | Low variance, high bias |
| Complexity | Simple | Complex |
| Interpretability | High | Low |
📚 Further Reading
For a deeper dive into ensemble learning, check out our tutorial:
Ensemble Learning Techniques
Explore more about machine learning algorithms here:
Machine Learning Overview