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Free
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Learning rate
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Shrinkage rate (how much will the weights be adjusted every iteration).
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Number of leaves
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Maximum number of leaves in one tree.
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Lambda L1
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L1 regularization.
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Lambda L2
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L2 regularization.
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Feature fraction
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Randomly select part of the predictors on each iteration.
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Fixed
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Early stopping
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The cross-validation score needs to improve at least every n round to continue with the next boosting iteration.
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Maximum depth
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Maximum tree depth (note that it is less relevant here since the tree grows leaf-wise).
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Minimum data
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Minimal number of records in one leaf. A higher number prevents overfitting.
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Bagging fraction
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Randomly select part of the data without resampling.
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Bagging frequency
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Per how many rounds should bagging be applied.
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Unbalanced data
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Does data need to be balanced or not.
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