From 1864fab59272f47a4024fa6529a024cba5e95c97 Mon Sep 17 00:00:00 2001 From: Harvey Date: Mon, 6 Dec 2021 23:46:44 -0800 Subject: [PATCH] Minor edits to Parameters doc page. (#7500) * bost -> both * doc improvement * use original filename * syntax highlight false * missed a few highlights --- doc/parameter.rst | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/doc/parameter.rst b/doc/parameter.rst index f4b56949f..97f6232d8 100644 --- a/doc/parameter.rst +++ b/doc/parameter.rst @@ -38,7 +38,7 @@ General Parameters is displayed as warning message. If there's unexpected behaviour, please try to 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 a parameter is used or not. The feature is still experimental. It's expected to have @@ -49,7 +49,7 @@ General Parameters - Number of parallel threads used to run XGBoost. When choosing it, please keep thread contention and hyperthreading in mind. -* ``disable_default_eval_metric`` [default=``false``] +* ``disable_default_eval_metric`` [default= ``false``] - Flag to disable default metric. Set to 1 or ``true`` to disable. @@ -71,8 +71,8 @@ Parameters for Tree Booster * ``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. - - range: [0,∞] (0 is only accepted in ``lossguide`` growing policy when tree_method is set as ``hist`` or ``gpu_hist``) + - 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``) * ``min_child_weight`` [default=1] @@ -208,7 +208,7 @@ Parameters for Tree Booster - Maximum number of discrete bins to bucket continuous features. - Increasing this number improves the optimality of splits at the cost of higher computation time. -* ``predictor``, [default=``auto``] +* ``predictor``, [default= ``auto``] - The type of predictor algorithm to use. Provides the same results but allows the use of GPU or CPU. @@ -221,23 +221,24 @@ Parameters for Tree Booster recommended for performing prediction tasks. * ``num_parallel_tree``, [default=1] + - Number of parallel trees constructed during each iteration. This option is used to support boosted random forest. * ``monotone_constraints`` - - Constraint of variable monotonicity. See tutorial for more information. + - Constraint of variable monotonicity. See :doc:`/tutorials/monotonic` for more information. * ``interaction_constraints`` - 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 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 ================================================================ -* ``single_precision_histogram``, [default=``false``] +* ``single_precision_histogram``, [default= ``false``] - Use single precision to build histograms instead of double precision. @@ -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: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. - - ``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) @@ -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. -* ``seed_per_iteration`` [default=false] +* ``seed_per_iteration`` [default= ``false``] - Seed PRNG determnisticly via iterator number.