What Are the Challenges of Using Random Forest in Cancer Research?
Despite its advantages, using random forests in cancer research comes with challenges: - Computationally Intensive: Training multiple trees can be resource-intensive, requiring significant computation power and memory. - Interpretability: While random forests can identify important features, they are often considered "black-box" models, making it difficult to interpret the relationships between variables and outcomes. - Data Imbalance: Cancer datasets may have imbalanced classes (e.g., more healthy samples than cancerous ones), which can affect the performance of the model.
