To enhance model performance, which method is commonly used for handling missing values?

Imputation is a widely-used technique for handling missing values in datasets when preparing for model training. The primary goal of imputation is to fill in the missing data points with estimated values based on the existing information in the dataset. By accurately replacing missing values, imputation helps to maintain the integrity of the data and can significantly improve the performance and reliability of predictive models.

This approach can involve various methods, such as using the mean, median, or mode for numerical data, or utilizing more complex algorithms like k-Nearest Neighbors (k-NN) or regression methods to predict the missing values. By addressing the incomplete data issue, imputation minimizes the potential bias that could arise if the model were trained on a dataset with missing information, ultimately leading to more robust and accurate outcomes in predictive modeling.

In contrast, transformation refers to changing the scale or distribution of the dataset, regularization involves techniques used to reduce overfitting in models, and consolidation typically includes merging data sources but does not directly deal with missing values. Thus, imputation stands out as the most appropriate method for enhancing model performance in the context of missing data.