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Address some sphinx warnings and errors, add doc for building doc. (#4589)

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Jiaming Yuan
2019-06-21 06:07:36 +08:00
committed by Philip Hyunsu Cho
parent 6125521caf
commit 9494950ee7
7 changed files with 44 additions and 23 deletions
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17
doc/parameter.rst
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@@ -83,17 +83,12 @@ Parameters for Tree Booster
- range: (0,1]
* ``colsample_bytree``, ``colsample_bylevel``, ``colsample_bynode`` [default=1]
- This is a family of parameters for subsampling of columns.
- All ``colsample_by*`` parameters have a range of (0, 1], the default value of 1, and
specify the fraction of columns to be subsampled.
- ``colsample_bytree`` is the subsample ratio of columns when constructing each
tree. Subsampling occurs once for every tree constructed.
- ``colsample_bylevel`` is the subsample ratio of columns for each level. Subsampling
occurs once for every new depth level reached in a tree. Columns are subsampled from
the set of columns chosen for the current tree.
- ``colsample_bynode`` is the subsample ratio of columns for each node
(split). Subsampling occurs once every time a new split is evaluated. Columns are
subsampled from the set of columns chosen for the current level.
- All ``colsample_by*`` parameters have a range of (0, 1], the default value of 1, and specify the fraction of columns to be subsampled.
- ``colsample_bytree`` is the subsample ratio of columns when constructing each tree. Subsampling occurs once for every tree constructed.
- ``colsample_bylevel`` is the subsample ratio of columns for each level. Subsampling occurs once for every new depth level reached in a tree. Columns are subsampled from the set of columns chosen for the current tree.
- ``colsample_bynode`` is the subsample ratio of columns for each node (split). Subsampling occurs once every time a new split is evaluated. Columns are subsampled from the set of columns chosen for the current level.
- ``colsample_by*`` parameters work cumulatively. For instance,
the combination ``{'colsample_bytree':0.5, 'colsample_bylevel':0.5,
'colsample_bynode':0.5}`` with 64 features will leave 8 features to choose from at
@@ -294,7 +289,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
* ``objective`` [default=reg:squarederror]
- ``reg:squarederror``: regression with squared loss
- ``reg:squarederror``: regression with squared loss.
- ``reg:squaredlogerror``: regression with squared log loss :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2`. All input labels are required to be greater than -1. Also, see metric ``rmsle`` for possible issue with this objective.
- ``reg:logistic``: logistic regression
- ``binary:logistic``: logistic regression for binary classification, output probability