Your question is Handling High-Dimensional Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you handle the challenges of working with high-dimensional data?
Explain and demonstrate a practical workflow for reducing overfitting, controlling computational cost, handling sparse or correlated features, and preserving predictive signal. Your response should address preprocessing, feature selection or dimensionality reduction, regularization, model selection, validation, and interpretability. Include Python code that can be adapted to a real supervised learning dataset, and explain how you would choose between competing approaches.
