new Loss()
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Methods
computeError(modelOrPrediction, dataOrLabel) → {float}
If TreeEnsembleModel, RDD parameters are supplied:
Method to calculate error of the base learner for the gradient boosting calculation or
Note: This method is not used by the gradient boosting algorithm but is useful for debugging purposes.
If float, float parameters are supplied:
Method to calculate loss when the predictions are already known.
Note: This method is used in the method evaluateEachIteration to avoid recomputing the predicted values from previously fit trees.
Parameters:
| Name | Type | Description |
|---|---|---|
modelOrPrediction |
TreeEnsembleModel | float | Model of the weak learner or predicted label (predict only valid with label param). |
dataOrLabel |
module:eclairjs.RDD | float | Training dataset: RDD of LabeledPoint or true label (use of label only valid with prediction param). |
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Returns:
- Type
- float
gradient(prediction, label) → {float}
Method to calculate the gradients for the gradient boosting calculation.
Parameters:
| Name | Type | Description |
|---|---|---|
prediction |
float | |
label |
float |
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Returns:
- Type
- float