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Fix spelling in documents (#6948)

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* Update roxygen2 doc.

Co-authored-by: fis <jm.yuan@outlook.com>
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Andrew Ziem
2021-05-11 06:44:36 -06:00
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parent 2a9979e256
commit 3e7e426b36
100 changed files with 284 additions and 284 deletions
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doc/parameter.rst
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@@ -191,7 +191,7 @@ Parameters for Tree Booster
- Choices: ``default``, ``update``
- ``default``: The normal boosting process which creates new trees.
- ``update``: Starts from an existing model and only updates its trees. In each boosting iteration, a tree from the initial model is taken, a specified sequence of updaters is run for that tree, and a modified tree is added to the new model. The new model would have either the same or smaller number of trees, depending on the number of boosting iteratons performed. Currently, the following built-in updaters could be meaningfully used with this process type: ``refresh``, ``prune``. With ``process_type=update``, one cannot use updaters that create new trees.
- ``update``: Starts from an existing model and only updates its trees. In each boosting iteration, a tree from the initial model is taken, a specified sequence of updaters is run for that tree, and a modified tree is added to the new model. The new model would have either the same or smaller number of trees, depending on the number of boosting iterations performed. Currently, the following built-in updaters could be meaningfully used with this process type: ``refresh``, ``prune``. With ``process_type=update``, one cannot use updaters that create new trees.
* ``grow_policy`` [default= ``depthwise``]
@@ -362,15 +362,15 @@ 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)
- ``max_delta_step`` is set to 0.7 by default in Poisson regression (used to safeguard optimization)
- ``survival:cox``: Cox regression for right censored survival time data (negative values are considered right censored).
Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function ``h(t) = h0(t) * HR``).
- ``survival:aft``: Accelerated failure time model for censored survival time data.
See :doc:`/tutorials/aft_survival_analysis` for details.
- ``aft_loss_distribution``: Probabilty Density Function used by ``survival:aft`` objective and ``aft-nloglik`` metric.
- ``aft_loss_distribution``: Probability Density Function used by ``survival:aft`` objective and ``aft-nloglik`` metric.
- ``multi:softmax``: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)
- ``multi:softprob``: same as softmax, but output a vector of ``ndata * nclass``, which can be further reshaped to ``ndata * nclass`` matrix. The result contains predicted probability of each data point belonging to each class.
- ``rank:pairwise``: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized