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What is weight in XGBoost?
It won’t be the same. Check this for how XGBoost handles weights: https://github.com/dmlc/xgboost/issues/144. 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.
Is XGBoost a tree based classifier?
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. The algorithm differentiates itself in the following ways: A wide range of applications: Can be used to solve regression, classification, ranking, and user-defined prediction problems.
Why are sample weights not used in XGBoost?
Basically the fitting of the model does not seem to take into account the sample_weight parameter – it starts at an AUC of 0.5 and drops from there, recommending 0, or 1 n_estimators. There is nothing wrong with the underlying data – we have constructed a very good model using sample weights using another tool, getting a good Gini.
Why is XGBoost multiplies gradient and Hessian by weights?
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. That’s why (as you will see in the discussion I linked above) xgboost multiplies the gradient and the hessian by the weights, not the target values.
How to replace model definition with UPD in XGBoost?
Just replace the lines starting with your model definition by the following code: UPD: The xgboost community is aware of it and there is a discussion and even a PR for it: https://github.com/dmlc/xgboost/issues/1804. However, this was never propagated to v0.71 for some reason.
Why is XGBoost not sensitive to monotonic transformations?
XGBoost is not sensitive to monotonic transformations of its features for the same reason that decision trees and random forests are not: the model only needs to pick “cut points” on features to split a node. Splits are not sensitive to monotonic transformations: defining a split on one scale has a corresponding split on the transformed scale.