Several algorithms are commonly employed in cancer model training:
- Logistic Regression: Useful for binary classification problems, such as predicting the presence or absence of cancer. - Decision Trees and Random Forests: Good for handling complex datasets with many variables. - Support Vector Machines (SVM): Effective for high-dimensional data. - Neural Networks and Deep Learning: Particularly useful for image analysis and genomic data.
