I am currently working on a predictive modeling project using the gbm package in R and have encountered a challenge regarding missing values in one of my predictor variables. I would appreciate your insights and recommendations on the best practices for handling this issue.
Context:
- Predictor Variable: total_Visits – representing the total number of visits a patient had between 1 to 6 months prior to diagnosis.
- Issue: A significant portion of the dataset has NA values (~ 85%) for this predictor, indicating that no visit records were found for those patients during the specified period.
Considerations:
- Imputation: Converting NA values to zero results in a highly skewed distribution, where the first quartile, median, and third quartile are all zero. This skewness may adversely affect the model's performance.
- Retention of NA Values: Keeping NA values as they are could allow the gbm function to handle them internally. However, I am concerned about how this might influence the model's interpretation and accuracy.
Questions:
- Best Practices: What are the recommended approaches for handling such missing values in predictor variables when using gradient boosting models in R?
- Impact on Model Performance: How does the presence of NA values in predictor variables affect the performance and interpretability of gradient boosting models?
- Alternative Strategies: Are there other strategies, such as creating indicator variables for missingness or employing advanced imputation techniques, that could be beneficial in this context?
I am seeking guidance on how to address these missing values effectively to ensure the robustness and reliability of my predictive model. Any advice, references, or examples from your experiences would be greatly appreciated.
Thank you in advance for your assistance.
