From ee3d680e8908225b3ffb3bd5bad2cabe71d1a6d3 Mon Sep 17 00:00:00 2001 From: Pierre PACI Date: Sat, 17 Jun 2017 18:22:59 +0200 Subject: [PATCH] Fix Typo in documentation (#2416) The objective section was missing a space and thus all the bullet were are the same level. --- doc/parameter.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/doc/parameter.md b/doc/parameter.md index 08b157619..e5a8b5697 100644 --- a/doc/parameter.md +++ b/doc/parameter.md @@ -155,17 +155,17 @@ Learning Task Parameters ------------------------ Specify the learning task and the corresponding learning objective. The objective options are below: * objective [ default=reg:linear ] - - "reg:linear" --linear regression - - "reg:logistic" --logistic regression - - "binary:logistic" --logistic regression for binary classification, output probability - - "binary:logitraw" --logistic regression for binary classification, output score before logistic transformation - - "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) - - "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" --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). + - "reg:linear" --linear regression + - "reg:logistic" --logistic regression + - "binary:logistic" --logistic regression for binary classification, output probability + - "binary:logitraw" --logistic regression for binary classification, output score before logistic transformation + - "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) + - "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" --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 ] - the initial prediction score of all instances, global bias - for sufficient number of iterations, changing this value will not have too much effect.