There are several reasons why random forest is particularly useful in histology:
- Accuracy: Random forest tends to provide high accuracy in classification tasks due to its ensemble nature. - Robustness: The algorithm is less likely to overfit compared to other models, making it reliable for varied and complex histological data. - Interpretability: While individual trees may be complex, the overall model can provide insights into which features are most important for classification. - Scalability: Random forest can handle large datasets effectively, which is common in histological studies.