ENH: Add warnings for small target values in RandomForestRegressor #30538
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Reference Issues/PRs
Fixes #29922
What does this implement/fix? Explain your changes.
This PR addresses the potential numerical instability and performance issues when using very small target values in
RandomForestRegressorand data with very small ranges. The following changes have been made:Documentation Update:
RandomForestRegressordocumentation about the potential numerical issues with very small target values (e.g.,1e-8or smaller).Validation Enhancement:
_check_data_rangeinsklearn/utils/validation.pyto detect and warn about data with very small ranges.validate_datafunction to warn users if their target variable (y) has a very small range.Any other comments?
To further illustrate the impact of small target values and data range, I generated a heatmap that visualizes the R² score for different scaling factors applied to X and Y. The results highlight numerical instability when very small scaling factors (e.g., 1e-8 or smaller) are used for the targets Y. This reinforces the need to warn users about potential issues and recommend scaling their data appropriately.
