A random forest is a type of ensemble learning method that combines multiple decision trees. It is a more sophisticated approach than bagging because it also randomly selects features to split on at each node of the decision tree. This helps to reduce the correlation between the decision trees, which makes the forest more robust to overfitting.
In general, a random forest is better than 100 decision trees with bagging. This is because the random forest is more robust to overfitting and it can often achieve better accuracy. However, the random forest is also more computationally expensive than bagging.
Here is a table summarizing the key differences between 100 decision trees with bagging and random forest:
| Feature | 100 decision trees with bagging | Random forest |
|---|---|---|
| Number of trees | 100 | Multiple |
| Feature selection | All features | Randomly selected features |
| Correlation between trees | High | Low |
| Overfitting | More prone | Less prone |
| Accuracy | Can be good | Often better |
| Computational complexity | Less computationally expensive | More computationally expensive |
Ultimately, the best approach to use will depend on the specific problem being solved. If computational resources are limited, then 100 decision trees with bagging may be a better choice. However, if the goal is to achieve the best possible accuracy, then a random forest is the better choice.
Photo by zhang kaiyv
