Enhancing Model Performance and Interpretability through Feature Selection and Missing Value Imputation

Missing data is a common problem in machine learning, where some features or values are not available for training or testing models. To address this issue, researchers have proposed various methods, including feature selection, imputation, and ignore methods. Feature selection aims to identify the most informative features and reduce the impact of missing values on model performance. Imputation methods estimate missing values using statistical techniques or machine learning algorithms, while ignoring method leverages known information from missing data to handle it directly in the modeling process. The proposed method combines these approaches to provide a more robust solution for handling missing data. In this article, we discuss the different types of missing mechanisms and how they are addressed using these methods. By understanding these mechanisms and their corresponding solutions, machine learning practitioners can develop more accurate and reliable models for various applications.