17 lines
1.1 KiB
Markdown
17 lines
1.1 KiB
Markdown
Regression
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====
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Using XGBoost for regression is very similar to using it for binary classification. We suggest that you can refer to the [binary classification demo](../binary_classification) first. In XGBoost if we use negative log likelihood as the loss function for regression, the training procedure is same as training binary classifier of XGBoost.
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### Tutorial
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The dataset we used is the [computer hardware dataset from UCI repository](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware). The demo for regression is almost the same as the [binary classification demo](../binary_classification), except a little difference in general parameter:
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```
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# General parameter
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# this is the only difference with classification, use reg:squarederror to do linear regression
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# when labels are in [0,1] we can also use reg:logistic
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objective = reg:squarederror
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...
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```
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The input format is same as binary classification, except that the label is now the target regression values. We use linear regression here, if we want use objective = reg:logistic logistic regression, the label needed to be pre-scaled into [0,1].
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