Implement basic feature sampling in tree model #212
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RAMitchell
changed the title
seberg
reviewed
Feb 21, 2025
| feature_fraction : | ||
| If float, the subsampled fraction of features considered in building this model. | ||
| Users may implement an arbitrary function returning a cupynumeric array of | ||
| booleans of shape `(n_features,)` to specify the feature subset. |
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| booleans of shape `(n_features,)` to specify the feature subset. | |
| booleans of shape ``(n_features,)`` to specify the feature subset. |
Should we say how this is rounded?
| @@ -33,6 +33,10 @@ class Tree(BaseModel): | |||
| split_samples : int | |||
| The number of data points to sample for each split decision. | |||
| Max value is 2048 due to constraints on shared memory in GPU kernels. | |||
| feature_fraction : | |||
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Seems like you wanted to put this. (I'll note that since we do have type hints in think it is also completely fine to just put nothing/remove the : even.)
| @@ -57,6 +61,7 @@ def __init__( | |||
| *, | |||
| max_depth: int = 8, | |||
| split_samples: int = 256, | |||
| feature_fraction: Union[float, Callable[..., cn.array]] = 1.0, | |||
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| feature_fraction: Union[float, Callable[..., cn.array]] = 1.0, | |
| feature_fraction: Union[float, Callable[[], cn.array]] = 1.0, |
seberg
approved these changes
Feb 21, 2025
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Implements #202