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[R] Fix global feature importance and predict with 1 sample. (#7394)

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* [R] Fix global feature importance.

* Add implementation for tree index.  The parameter is not documented in C API since we
should work on porting the model slicing to R instead of supporting more use of tree
index.

* Fix the difference between "gain" and "total_gain".

* debug.

* Fix prediction.
This commit is contained in:
Jiaming Yuan
2021-11-05 10:07:00 +08:00
committed by GitHub
parent 48aff0eabd
commit c968217ca8
11 changed files with 119 additions and 49 deletions
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doc/prediction.rst
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@@ -32,8 +32,8 @@ After 1.4 release, we added a new parameter called ``strict_shape``, one can set
- When using ``output_margin`` to avoid transformation and ``strict_shape`` is set to ``True``:
Similar to the previous case, output is a 2-dim array, except for that ``multi:softmax``
has equivalent output of ``multi:softprob`` due to dropped transformation. If strict
shape is set to False then output can have 1 or 2 dim depending on used model.
has equivalent output shape of ``multi:softprob`` due to dropped transformation. If
strict shape is set to False then output can have 1 or 2 dim depending on used model.
- When using ``preds_contribs`` with ``strict_shape`` set to ``True``: