What are structurally missing data?

Structurally missing data is logically undefined and not random, often due to a lack of applicable data fields. It is not due to error or randomness but logically cannot exist under certain conditions.

Handling Structurally Missing Data:

1. Recoding and Filtering: Address structurally missing data by recoding or filtering out instances.
2. Modeling Considerations: Incorporate variables with missing data as interaction terms, without main effect.
3. Population Considerations: Recognize that missing data represents different populations, and informs decision on data drop or omission.

Understanding and handling structurally missing data is crucial for accurate analysis and modeling, allowing researchers to make informed decisions without bias or inaccuracies.

• MCAR (Missing Completely At Random): Uniform absence of data across all observations, reducing analyzable population and statistical power but not introducing bias.
• MAR (Missing At Random): Missing data linked to observed data but not the missing data, requiring methods like Multiple Imputation and Maximum Likelihood for accurate handling.
• NMAR (Not Missing At Random): Complex scenario where missing data is dependent on unobserved values, challenging standard imputation techniques and requiring specialized methods for accurate analysis.

Data imputation is a critical challenge in machine learning, with missing values impacting statistical modelling. Random Forest, an ensemble learning method, is a robust solution for accurate predictions, particularly in healthcare. It can handle classification and regression problems, and it is more nuanced than traditional methods. It can handle nan values and decision tree missing values, providing a reliable strategy for data imputation. In this article, we will see how we can handle missing values explicitly using Random Forest.