Kumo identifies missing values as entries that are completely left blank, and will not treat columns marked by special strings (e.g., “NaN”, “none”, “N/A”) as missing values. For numeric columns, missing values are commonly filled with invalid number (e.g., -1) to signify their absence. These entries should be cleared out or left blank if you would like them to be considered as missing data.Documentation Index
Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
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Data
How does Kumo handle missing values in my dataset?
Preventing data leakage and handling time correctnessWhat mechanisms does Kumo provide to detect data leakage?