Document gblinear parameters: feature_selector and top_k (#3780)

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Philip Hyunsu Cho 2018-10-08 22:41:54 -07:00 committed by GitHub
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@ -248,6 +248,20 @@ Parameters for Linear Booster (``booster=gblinear``)
- ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run. - ``shotgun``: Parallel coordinate descent algorithm based on shotgun algorithm. Uses 'hogwild' parallelism and therefore produces a nondeterministic solution on each run.
- ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution. - ``coord_descent``: Ordinary coordinate descent algorithm. Also multithreaded but still produces a deterministic solution.
* ``feature_selector`` [default= ``cyclic``]
- Feature selection and ordering method
* ``cyclic``: Deterministic selection by cycling through features one at a time.
* ``shuffle``: Similar to ``cyclic`` but with random feature shuffling prior to each update.
* ``random``: A random (with replacement) coordinate selector.
* ``greedy``: Select coordinate with the greatest gradient magnitude. It has ``O(num_feature^2)`` complexity. It is fully deterministic. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter. Doing so would reduce the complexity to ``O(num_feature*top_k)``.
* ``thrifty``: Thrifty, approximately-greedy feature selector. Prior to cyclic updates, reorders features in descending magnitude of their univariate weight changes. This operation is multithreaded and is a linear complexity approximation of the quadratic greedy selection. It allows restricting the selection to ``top_k`` features per group with the largest magnitude of univariate weight change, by setting the ``top_k`` parameter.
* ``top_k`` [default=0]
- The number of top features to select in ``greedy`` and ``thrifty`` feature selector. The value of 0 means using all the features.
Parameters for Tweedie Regression (``objective=reg:tweedie``) Parameters for Tweedie Regression (``objective=reg:tweedie``)
============================================================= =============================================================
* ``tweedie_variance_power`` [default=1.5] * ``tweedie_variance_power`` [default=1.5]

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@ -241,7 +241,7 @@ class CyclicFeatureSelector : public FeatureSelector {
}; };
/** /**
* \brief Similar to Cyclyc but with random feature shuffling prior to each update. * \brief Similar to Cyclic but with random feature shuffling prior to each update.
* \note Its randomness is controllable by setting a random seed. * \note Its randomness is controllable by setting a random seed.
*/ */
class ShuffleFeatureSelector : public FeatureSelector { class ShuffleFeatureSelector : public FeatureSelector {