Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Minor edits to Parameters doc page. (#7500)

Browse Source 
* bost -> both

* doc improvement

* use original filename

* syntax highlight false

* missed a few highlights
This commit is contained in:
Harvey 2021-12-06 23:46:44 -08:00 committed by GitHub
parent 021f8bf28b
commit 1864fab592
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 11 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
doc/parameter.rst
View File

@ -38,7 +38,7 @@ General Parameters
is displayed as warning message. If there's unexpected behaviour, please try to is displayed as warning message. If there's unexpected behaviour, please try to
increase value of verbosity. increase value of verbosity.
* ``validate_parameters`` [default to false, except for Python, R and CLI interface] * ``validate_parameters`` [default to ``false``, except for Python, R and CLI interface]
- When set to True, XGBoost will perform validation of input parameters to check whether - When set to True, XGBoost will perform validation of input parameters to check whether
a parameter is used or not. The feature is still experimental. It's expected to have a parameter is used or not. The feature is still experimental. It's expected to have
@ -71,8 +71,8 @@ Parameters for Tree Booster
* ``max_depth`` [default=6] * ``max_depth`` [default=6]
- Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in ``lossguide`` growing policy when tree_method is set as ``hist`` or ``gpu_hist`` and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree. - Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in ``lossguide`` growing policy when ``tree_method`` is set as ``hist`` or ``gpu_hist`` and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.
- range: [0,∞] (0 is only accepted in ``lossguide`` growing policy when tree_method is set as ``hist`` or ``gpu_hist``) - range: [0,∞] (0 is only accepted in ``lossguide`` growing policy when ``tree_method`` is set as ``hist`` or ``gpu_hist``)
* ``min_child_weight`` [default=1] * ``min_child_weight`` [default=1]
@ -221,18 +221,19 @@ Parameters for Tree Booster
recommended for performing prediction tasks. recommended for performing prediction tasks.
* ``num_parallel_tree``, [default=1] * ``num_parallel_tree``, [default=1]
- Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. - Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.
* ``monotone_constraints`` * ``monotone_constraints``
- Constraint of variable monotonicity. See tutorial for more information. - Constraint of variable monotonicity. See :doc:`/tutorials/monotonic` for more information.
* ``interaction_constraints`` * ``interaction_constraints``
- Constraints for interaction representing permitted interactions. The constraints must - Constraints for interaction representing permitted interactions. The constraints must
be specified in the form of a nest list, e.g. ``[[0, 1], [2, 3, 4]]``, where each inner be specified in the form of a nest list, e.g. ``[[0, 1], [2, 3, 4]]``, where each inner
list is a group of indices of features that are allowed to interact with each other. list is a group of indices of features that are allowed to interact with each other.
See tutorial for more information See :doc:`/tutorials/feature_interaction_constraint` for more information.
Additional parameters for ``hist`` and ``gpu_hist`` tree method Additional parameters for ``hist`` and ``gpu_hist`` tree method
================================================================ ================================================================
@ -348,7 +349,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``binary:logistic``: logistic regression for binary classification, output probability - ``binary:logistic``: logistic regression for binary classification, output probability
- ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation - ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
- ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities. - ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
- ``count:poisson`` --poisson regression for count data, output mean of Poisson distribution - ``count:poisson``: Poisson regression for count data, output mean of Poisson distribution.
- ``max_delta_step`` is set to 0.7 by default in Poisson regression (used to safeguard optimization) - ``max_delta_step`` is set to 0.7 by default in Poisson regression (used to safeguard optimization)
@ -418,7 +419,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- Random number seed. This parameter is ignored in R package, use `set.seed()` instead. - Random number seed. This parameter is ignored in R package, use `set.seed()` instead.
* ``seed_per_iteration`` [default=false] * ``seed_per_iteration`` [default= ``false``]
- Seed PRNG determnisticly via iterator number. - Seed PRNG determnisticly via iterator number.