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Fix minor typos in parameters.md (#1521)

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Dex Groves 2016-08-29 14:02:03 +01:00 committed by Nan Zhu
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doc/parameter.md
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@ -54,22 +54,22 @@ Parameters for Tree Booster
* alpha [default=0]
- L1 regularization term on weights, increase this value will make model more conservative.
* tree_method, string [default='auto']
- The tree constructtion algorithm used in XGBoost(see description in the [reference paper](http://arxiv.org/abs/1603.02754))
- The tree construction algorithm used in XGBoost(see description in the [reference paper](http://arxiv.org/abs/1603.02754))
- Distributed and external memory version only support approximate algorithm.
- Choices: {'auto', 'exact', 'approx'}
- 'auto': Use heuristic to choose faster one.
- For small to medium dataset, exact greedy will be used.
- For very large-dataset, approximate algorithm will be choosed.
- For very large-dataset, approximate algorithm will be chosen.
- Because old behavior is always use exact greedy in single machine,
user will get a message when approximate algorithm is choosed to notify this choice.
user will get a message when approximate algorithm is chosen to notify this choice.
- 'exact': Exact greedy algorithm.
- 'approx': Approximate greedy algorithm using sketching and histogram.
* sketch_eps, [default=0.03]
- This is only used for approximate greedy algorithm.
- This roughly translated into ```O(1 / sketch_eps)``` number of bins.
Compared to directly select number of bins, this comes with theoretical ganrantee with sketch accuracy.
- Usuaully user do not have to tune this.
but consider set to lower number for more accurate enumeration.
Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy.
- Usually user does not have to tune this.
but consider setting to a lower number for more accurate enumeration.
- range: (0, 1)
* scale_pos_weight, [default=0]
- Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See [Parameters Tuning](how_to/param_tuning.md) for more discussion. Also see Higgs Kaggle competition demo for examples: [R](../demo/kaggle-higgs/higgs-train.R ), [py1](../demo/kaggle-higgs/higgs-numpy.py ), [py2](../demo/kaggle-higgs/higgs-cv.py ), [py3](../demo/guide-python/cross_validation.py)
@ -121,7 +121,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- "reg:gamma" --gamma regression for severity data, output mean of gamma distribution
* base_score [ default=0.5 ]
- the initial prediction score of all instances, global bias
- for sufficent 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.
* 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
@ -137,7 +137,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- "map":[Mean average precision](http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision)
- "ndcg@n","map@n": n can be assigned as an integer to cut off the top positions in the lists for evaluation.
- "ndcg-","map-","ndcg@n-","map@n-": In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding "-" in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
training repeatively
training repeatedly
- "gamma-deviance": [residual deviance for gamma regression]
* seed [ default=0 ]
- random number seed.