Handling Missing Data in Decision Trees

Decision trees handle missing data by either ignoring instances with missing values, imputing them using statistical measures, or creating separate branches. During prediction, the tree follows the training strategy, applying imputation or navigating a dedicated branch for instances with missing data.

Types of Missing Data

Before tackling strategies, it’s crucial to understand the various types of missing data:

• Missing Completely at Random (MCAR): Sporadic missing data points unrelated to known or unknown factors. In MCAR, the occurrence of missing data is entirely random and unrelated to any observed or unobserved factors in the dataset. The missing values are essentially a result of a random process, and there’s no systematic reason for their absence.
• Missing at Random (MAR). In MAR, the probability of missing data depends on the observed variables in the dataset, but once those variables are considered, the missingness is random. In other words, the missing values can be predicted or explained by other observed variables, ensuring randomness after accounting for these factors.
• Missing Not at Random (MNAR): A systematic pattern exists between the missing data and the missing values themselves.

Decision trees, a popular and powerful tool in data science and machine learning, are adept at handling both regression and classification tasks. However, their performance can suffer due to missing or incomplete data, which is a frequent challenge in real-world datasets. This article delves into the intricacies of handling missing data in decision tree models and explores strategies to mitigate its impact.