Comments (3)
tqchen answer:
In higgs, you want to do weight aware objective function. Each instances is have a weight. The build-in logitraw takes that into consideration, also there is a scale_pos_weight which rescale the weight of positive examples.
So the simple example is not same as logitraw in this case. It is consistent in other cases where you do not have weight, for example the mushroom dataset as illustrated
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So then there isn’t a way to pass the weights to a custom function?
Could I use ‘getinfo’ for obtain the weights from the function and rescaled them in any way?
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tqchen answer:
Yes you can use getinfo to get weight. I am not sure about adaboost loss you mentioned. I guess if you can write the loss down, it could be possible you write an instance weighted version.
As long as you can define gradient, you can customize it.
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Related Issues (20)
- An error occurred: jemalloc unsupported system page size HOT 2
- Make external CUDA stream wait.
- Docs on params and Python tutorial for regression HOT 1
- Docs for custom objectives could cover more details HOT 9
- Which one is `y_true` when I use a custom loss for regression mode. HOT 3
- clarification needed for model/saving loading HOT 2
- Missing XGBoostRanker in xgboost4j-spark jvm package HOT 3
- multi label support in Scala xgboost. HOT 8
- SparkXGBClassifier does not validate params HOT 6
- feature_weights only compatible with CPU ? HOT 1
- Improve XGBoost quantile predictions HOT 3
- Latest version training crashes HOT 12
- Error when trying to build HOT 4
- Model provides different results for different Python versions HOT 2
- error in the docs for ranking HOT 1
- Defining a callback to write hessians of train observations to a csv file HOT 2
- Slow inference on sphoradic stremaing data HOT 2
- XGBoost GPU Warning When Working with BayesSearchCV (XGBoost is running on: cuda:0, while the input data is on: cpu.)
- help installing xgboost with gpu HOT 2
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