Random Forest Vs Decision Tree
Property |
Random Forest |
Decision Tree |
---|---|---|
Nature |
Ensemble of multiple decision trees |
Single Decision Tree |
Interpretability |
Less interpretable due to ensemble nature. |
Highly interpretable. |
Overfitting |
Due to ensemble averaging it is less prone to overfitting. |
More prone to overfitting specially in case of deep trees. |
Training Time |
Since multiple trees are constructed, training time becomes more, and training speed becomes less. |
A single tree needs to be built and trained, hence faster in comparison. |
Stability to change |
Since overall average is taken due to ensemble, it is more stable to change. |
It becomes quite sensitive to variation in data. |
Predictive Time |
Multiple predictions, hence longer prediction time and slower prediction speed. |
Faster prediction as compared to random forest, since a single prediction is made. |
Performance |
Generally performs well on large datasets. |
It can perform well on small and large dataset as well. |
Handling Outliers |
Due to ensemble averaging more robust to outliers. |
It is more susceptible to outliers. |
Feature Importance |
Do not provide feature score directly rather uses ensemble to decide feature score. |
Provide feature score directly which are less reliable. |
Difference Between Random Forest and Decision Tree
Choosing the appropriate model is crucial when it comes to machine learning. A model that functions properly with one kind of dataset might not function well with another. Both Random Forest and Decision Tree are strong algorithms for applications involving regression and classification. The aim of the article is to cover the distinction between decision trees and random forests.
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