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Documentation Index

Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt

Use this file to discover all available pages before exploring further.

Kumo’s explainability can be incredibly useful for troubleshooting why your predictive query is underperforming for a particular subset of data, as well as discovering bias and areas where you might need to shore up your datasets. By analyzing these charts, you can better understand how individual values within each column positively or negatively affect the final prediction distribution. These statistics are calculated using the ground truth (i.e., the target labels)—which is what the predictive query learns to predict from—as well as the actual predicted values. For more information, check out our explainability page.