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Fix Typo in documentation (#2416)

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The objective section was missing a space and thus all the bullet were are the same level.
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Pierre PACI 2017-06-17 18:22:59 +02:00 committed by Tianqi Chen
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doc/parameter.md
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@ -155,17 +155,17 @@ Learning Task Parameters
------------------------ ------------------------
Specify the learning task and the corresponding learning objective. The objective options are below: 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:linear" --linear regression
- "reg:logistic" --logistic regression - "reg:logistic" --logistic regression
- "binary:logistic" --logistic regression for binary classification, output probability - "binary:logistic" --logistic regression for binary classification, output probability
- "binary:logitraw" --logistic regression for binary classification, output score before logistic transformation - "binary:logitraw" --logistic regression for binary classification, output score before logistic transformation
- "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)
- "multi:softmax" --set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes) - "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. - "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 - "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: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: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 - the initial prediction score of all instances, global bias
- for sufficient number of iterations, changing this value will not have too much effect. - for sufficient number of iterations, changing this value will not have too much effect.