Can you describe your approach and experience in feature selection for handling high-dimensional data?

How To Approach: Associate

1. Discuss real-world experience with feature selection in your work.
2. Describe the project, the challenge, and the goal of dimensionality reduction.
3. Explain the techniques used and why they were ideal for the situation.
4. Detail the results achieved, ideally with metrics to show impact.

Sample Response: Associate

In my role as a Data Scientist with DataTeam Solutions, I routinely handle datasets with high-dimensionality. One memorable project involved financial data with over 200 features related to customer behavior, transaction history, and account details. The goal was to improve the predictive power of our risk assessment models.

We decided to use the Lasso Regression approach, a method well-suited to such a task because of its ability to perform both prediction and feature selection simultaneously. It helped us to narrow down the most impactful features by shrinking coefficients of less important variables to zero, effectively reducing the number of variables.

The effect of this dimensionality reduction was significant. Our resulting risk assessment model's accuracy increased from 82% to 88%, and the model became vastly quicker to run. This demonstrated to me that careful and effective feature selection is not just about making models manageable, but it can also lead to substantial improvements in performance.