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For example, if I run a single round (nrounds=1), how does XGBoost go about making predictions? I thought it would simply return a linear regression model, but I quickly shot that theory down by working out an example.

Please note that I've already seen this and this and most of Google's top search results.

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gblinear uses linear functions, in contrast to dart which use tree based functions.

One primary difference between linear functions and tree-based functions is the decision boundary. Tree-based models decision boundaries are only piece-wise, perpendicular rules to each feature. Linear functions are monotonic lines through the feature space.

Tree-based functions tend to be more flexible. If the data is distributed closer to a line, then a linear function will be more parsimonious.

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  • $\begingroup$ Can you be more specific? Which linear functions does it use? $\endgroup$
    – Ben
    Jul 10 '21 at 21:12

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