A survey on missing data in machine learning

🤖 Plain-English Summary

Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%.

🔑 Key Findings

• Missing values occur because of various factors like missing completely at random, missing at random or missing not at random.
• All these may result from system malfunction during data collection or human error during data pre-processing.
• Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis.

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