What are the Benefits of Using Lasso in Cancer Research?
1. Feature Selection: Lasso effectively selects a subset of features by shrinking the coefficients of less relevant features to zero. This is particularly useful in high-dimensional cancer datasets, where the number of potential features can be overwhelming. 2. Improved Interpretability: By reducing the number of features, Lasso makes the model more interpretable, which is crucial for clinical applications where understanding the role of specific genes or proteins can guide treatment decisions. 3. Enhanced Prediction Accuracy: By avoiding overfitting, Lasso can enhance the prediction accuracy of the model, leading to more reliable diagnosis and prognosis predictions.
