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Add note on interpolation (#64)
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reidjohnson authored Jul 27, 2024
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5 changes: 5 additions & 0 deletions quantile_forest/_quantile_forest.py
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Expand Up @@ -491,6 +491,11 @@ def predict(
- If "nearest", then ``i`` or ``j``, whichever is nearest.
- If "midpoint", then ``(i + j) / 2``.
.. note::
When `max_samples_leaf=1`, the specific interpolation
options will depend on those available in the NumPy
`percentile` and `nanpercentile` methods.
weighted_quantile : bool, default=True
Calculate a weighted quantile. Weighted quantiles are computed by
assigning weights to each training sample, while unweighted
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