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I use the python implementation of XGBoost. One of the objectives is rank:pairwise and it minimizes the pairwise loss (Documentation). However, it does not say anything about the scope of the output. I see numbers between -10 and 10, but can it be in principle -inf to inf?
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search - How fit pairwise ranking models in XGBoost? - Data Science Stack Exchange As far as I know, to train learning to rank models, you need to have three things in the dataset: label or relevance group or query id feature vector For example, the Microsoft Learning to Rank d... Stack Exchange Network
When ranking with XGBoost there are three objective-functions; Pointwise, Pairwise, and Listwise. These three objective functions are different methods of finding the rank of a set of items, and each has its own strengths and weaknesses.
For classification and regression, XGBoost starts with an initial prediction usually 0.5, as shown in the below diagram. To find how good the prediction is, calculate the Loss function, by using the formula, For the given example, it came out to be 196.5.
If lambda = 0, the optimal output value is at the bottom of the parabola where the derivative is zero. XGBoost uses Second-Order Taylor Approximation for both classification and regression. The loss function containing output values can be approximated as follows:
It gives predicted score for ranking. However, the scores are valid for ranking only in their own groups. So we must set the groups for input data.
For esay ranking, refer to my project xgboostExtension
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