What are the techniques for handling missing data? How do I realize I am missing data? Are there general references on pretraining treatment of data?
Sunday, 10 May 2026
by Francesco Spanò
Handling missing data effectively is a foundational aspect of preparing datasets for machine learning tasks, as the quality and completeness of data directly influence model performance and the validity of predictive outcomes. Missing data can originate from various sources, including equipment malfunctions, human error, data corruption, or intentional omission. Understanding techniques for handling such instances,
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Tagged under:
Artificial Intelligence, Data Cleaning, Data Preprocessing, EDA, Google Cloud, Imputation, Machine Learning, MAR, MCAR, MISSING DATA, MNAR