Random forests offer several advantages in cancer diagnosis: 1. Accuracy: They provide high predictive accuracy, which is crucial for identifying cancerous cells accurately. 2. Handling High-Dimensional Data: Cancer datasets often contain a large number of features (genes, proteins). Random forests can manage these high-dimensional datasets effectively. 3. Feature Importance: They can rank the importance of features, helping researchers to focus on the most significant biomarkers. 4. Non-Linearity: Random forests can model complex, non-linear relationships that are often present in biological data.
