Data Collection: Gather high-dimensional data, such as gene expression or mutation data. Data Preprocessing: Clean and normalize the data to ensure it is suitable for analysis. Model Building: Use statistical software or programming languages like R or Python to build a Lasso regression model. Libraries such as scikit-learn in Python offer built-in functions for Lasso. Parameter Tuning: Adjust the regularization parameter to find the optimal balance between bias and variance. Model Validation: Validate the model using techniques like cross-validation to ensure its robustness.
