chg python back
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@ -53,7 +53,7 @@ plst = list(param.items())+[('eval_metric', 'ams@0.15')]
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watchlist = [ (xgmat,'train') ]
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# boost 120 tres
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num_round = 2
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num_round = 120
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print ('loading data end, start to boost trees')
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bst = xgb.train( plst, xgmat, num_round, watchlist );
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# save out model
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