How do I evaluate model performance with missing data?

How do I evaluate model performance with missing data?

Evaluating model performance with missing data can be challenging. You can use techniques like imputation, or analyze the data patterns.

What are some common imputation methods?

Common imputation methods include mean/mode imputation, k-nearest neighbors, and regression imputation.

How does missing data affect model accuracy?

Missing data can lead to biased estimates and reduced accuracy, as the model may not learn from incomplete information.

Are there specific metrics to use when assessing models with missing data?

Yes, metrics like RMSE, MAE, and R-squared can still be used, but it's important to consider how missing data was handled.