What are the weights in XGBoost?
3 Answers. Weighting means increasing the contribution of an example (or a class) to the loss function. That means the contribution of the gradient of that example will also be larger.
How does XGBoost handle imbalanced data?
XGBoost is an effective machine learning model, even on datasets where the class distribution is skewed. Before any modification or tuning is made to the XGBoost algorithm for imbalanced classification, it is important to test the default XGBoost model and establish a baseline in performance.
What does the parameter scale _ Pos _ weight in XGBoost do?
My question is rather simple what does the parameter scale_pos_weight in xgboost do? I know typically it should be s u m ( n e g a t i v e c a s e s) s u m ( p o s i t i v e c a s e s). Does it oversample the minority class by that ratio or does it undersample the majority class by inverse of that ratio? Or something else?
What is the mechanism of using param’scale _ Pos _ weight’?
For both xgboost and LightGBM, scale_pos_weight, if assuming perfectly balanced positive/negative samples, means that: number of positive samples = number of negative samples. which also means the following when using weights through scale_pos_weight: number of positive samples * sample_pos_weight = number of negative samples.
What should be the ideal scale Pos weight?
Generally, scale_pos_weight is the ratio of number of negative class to the positive class. Suppose, the dataset has 90 observations of negative class and 10 observations of positive class, then ideal value of scale_pos_weight should be 9. See the doc: http://xgboost.readthedocs.io/en/latest/parameter.html
When does sampling happen during cross validation in xgbcv?
Also I would like to know if during cross validation in xgbcv, does the sampling happen on the test part of the cross-validation also or only the train part is affected by scale_pos_weight? Because i’ve heard that sampling should never be applied to test as it gives over-optimistic results.