From 75ea07b8470a7947fd6e29a7425605800e46f99c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ren=C3=A9=20Scheibe?= Date: Mon, 7 Aug 2017 19:07:10 +0200 Subject: [PATCH] Fix parameter documentation inconsistencies (#2584) * fix indentation - otherwise list items are rendered incorrectly * consistency: no spaces inside square brackets --- doc/parameter.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/parameter.md b/doc/parameter.md index bc2ca39c1..31f5dd8f8 100644 --- a/doc/parameter.md +++ b/doc/parameter.md @@ -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 ] - - random number seed. +* 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