In what way does improper handling of missing data affect regularization techniques in a machine learning model?
- Depends on the regularization technique used.
- Does not impact regularization.
- Makes regularization less effective.
- Makes regularization more effective.
If missing data are not handled correctly, it can skew the model's learning and affect its complexity, making regularization techniques (which aim to control model complexity) less effective.
Loading...
Related Quiz
- You are given a dataset for an upcoming data analysis project. What initial EDA steps would you take before moving to model building?
- In a longitudinal study on childhood development, some data points are missing randomly due to logistical issues during data collection. How would you classify this missing data?
- The ______ of a scatter plot may indicate the presence of outliers in the dataset.
- Given a machine learning algorithm that is highly sensitive to the range of input values, which scaling technique should you implement?
- The degree of tailedness in a distribution is measured by _________.