Several algorithms are commonly used in supervised learning for cancer research. Some popular ones include:
- Support Vector Machines (SVM): Effective for high-dimensional data like gene expression profiles. - Random Forests: Useful for classifying different types of cancer and assessing feature importance. - Neural Networks: Particularly deep learning models, effective for image recognition tasks. - Logistic Regression: Often used for binary classification tasks, such as distinguishing between benign and malignant tumors. - k-Nearest Neighbors (k-NN): Simple but effective for certain types of cancer datasets.
