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Fix parameter documentation inconsistencies (#2584)

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* fix indentation - otherwise list items are rendered incorrectly
* consistency: no spaces inside square brackets
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René Scheibe 2017-08-07 19:07:10 +02:00 committed by Michaël Benesty
parent a0c5bde024
commit 75ea07b847
1 changed files with 6 additions and 6 deletions
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doc/parameter.md
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@ -154,7 +154,7 @@ Parameters for Tweedie Regression
Learning Task Parameters
------------------------
Specify the learning task and the corresponding learning objective. The objective options are below:
* objective [ default=reg:linear ]
* objective [default=reg:linear]
- "reg:linear" --linear regression
- "reg:logistic" --logistic regression
- "binary:logistic" --logistic regression for binary classification, output probability
@ -166,10 +166,10 @@ Specify the learning task and the corresponding learning objective. The objectiv
- "rank:pairwise" --set XGBoost to do ranking task by minimizing the pairwise loss
- "reg:gamma" --gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be [gamma-distributed](https://en.wikipedia.org/wiki/Gamma_distribution#Applications)
- "reg:tweedie" --Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be [Tweedie-distributed](https://en.wikipedia.org/wiki/Tweedie_distribution#Applications).
* base_score [ default=0.5 ]
* base_score [default=0.5]
- the initial prediction score of all instances, global bias
- for sufficient number of iterations, changing this value will not have too much effect.
* eval_metric [ default according to objective ]
* eval_metric [default according to objective]
- evaluation metrics for validation data, a default metric will be assigned according to objective (rmse for regression, and error for classification, mean average precision for ranking )
- User can add multiple evaluation metrics, for python user, remember to pass the metrics in as list of parameters pairs instead of map, so that latter 'eval_metric' won't override previous one
- The choices are listed below:
@ -190,13 +190,13 @@ training repeatedly
- "gamma-nloglik": negative log-likelihood for gamma regression
- "gamma-deviance": residual deviance for gamma regression
- "tweedie-nloglik": negative log-likelihood for Tweedie regression (at a specified value of the tweedie_variance_power parameter)
* seed [ default=0 ]
* seed [default=0]
- random number seed.
Command Line Parameters
-----------------------
The following parameters are only used in the console version of xgboost
* use_buffer [ default=1 ]
* use_buffer [default=1]
- Whether to create a binary buffer from text input. Doing so normally will speed up loading times
* num_round
- The number of rounds for boosting