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@ -33,10 +33,10 @@ def logregobj(preds, dtrain):
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# Take this in mind when you use the customization, and maybe you need write customized evaluation function
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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# return a pair metric_name, result
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# return a pair metric_name, result. The metric name must not contain a colon (:)
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# since preds are margin(before logistic transformation, cutoff at 0)
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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# training with customized objective, we can also do step by step training
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# simply look at xgboost.py's implementation of train
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bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
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bst = xgb.train(param, dtrain, num_round, watchlist, obj=logregobj, feval=evalerror)
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