IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

When considering how machine learning models handle missing data, understanding the strengths of different algorithms is crucial.

Regression models often utilize summary statistics, like mean or median values, to impute missing data, allowing them to function better in the presence of gaps in the dataset. This feature helps maintain the integrity of the model, as regression analyses typically assume all features are present and can lead to bias if missing values are not addressed.

Tree-based models, such as decision trees and random forests, are inherently robust to outliers and can handle missing values more effectively compared to other models. They can make decisions by splitting on feature values, which allows them to proceed down branches even if certain data points are missing, thus managing the impact of outliers without significantly degrading performance.

Neural networks, while powerful, can also be impacted by missing data. Advanced techniques within neural networks can identify patterns in the data that suggest bias due to missing values. By using methods like dropout or attention mechanisms, neural networks can adapt and adjust to the absence of data points, thereby helping to reduce bias in the predictions they make.

Given these characteristics, the statement that all these models have benefits regarding missing data holds true. Each type of model has unique approaches and capabilities, leading to the conclusion that